Based on this hypothesis , we propose an architecture for knowledge-based instance selection ( KBIS ) systems . It uses meta-learning to select the best suited instance selection method for each specific database , among several methods available . We carried out a study in order to verify whether this architecture can outperform the individual methods . Unlike the vanilla MixUp that samples interpolation policy from a predefined distribution , this paper introduces a meta-learning based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way . The validation set performance via meta-learning captures the underfitting issue , which provides more information to refine interpolation policy . Furthermore , we adapt our method for pseudo-label based semisupervised learning ( SSL ) along with a refined pseudo-labeling strategy . As there are several learning algorithms available , choosing one whose bias suits the current data best is not a trivial task . In this paper , we present a meta-learning based method for periodic algorithm selection in time-changing environments , named MetaStream . It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination . Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples . Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings . State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner . A negative side-effect of this sequential execution paradigm is that , as the environment becomes more and more challenging , and thus requiring more interaction episodes for the meta-learner , it needs the agent to reason over longer and longer time-scales . For part 3 ( classification ) , the submitted test run as well as our last official validation run were the result from a meta-model that assembled seven base deep-learning models : three based on Inception-V4 trained on our largest dataset ; three based on Inception trained on our smallest dataset ; and one based on ResNet-101 trained on our smaller dataset . The results of those component models were stacked in a meta-learning layer based on an SVM trained on the validation set of our largest dataset . Both batch and online annotation settings are experimented with . It is observed that the addition of this meta-learning layer produces much improved results that outperform best-known results . For the online setting , the T/T approach produces progressively better annotation with time , significantly outperforming the black-box as well as the static form of the meta-learner , on real-world data . In this paper , we show what types of incentive systems of rewards and punishments promote and maintain effective information behaviors or cooperative regimes in actual groupware . Our agent-based simulation demonstrates that a meta-reward system in which rewarders can gain other benefits for their own reward actions will probably encourage cooperation . Counterintuitively , our simulation also demonstrates that a system that applies sanctioning functions does not necessarily promote cooperation . We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories , followed by a mode seeking KL divergence to train a robust policy . We propose Meta Reward Learning ( MeRL ) to construct an auxiliary reward function that provides more refined feedback for learning . The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy . To this end , we develop a novel meta reinforcement learning framework , Hyper-Meta RL ( HMRL ) , for sparse reward RL problems . It consists of meta state embedding , meta reward shaping and meta policy learning modules : The cross-environment meta state embedding module constructs a common meta state space to adapt to different environments ; The meta state based environment-specific meta reward shaping effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity ; As a consequence , the meta policy then achieves better generalization and efficiency with the shaped meta reward . However , in many scenarios this reward signal might not be readily available for the target task , or the difference between the environments can be implicit and only observable from the dynamics . To this end , we introduce a method that allows for self-adaptation of learned policies : No-Reward Meta Learning ( NoRML ) . NoRML extends Model Agnostic Meta Learning ( MAML ) for RL and uses observable dynamics of the environment instead of an explicit reward function in MAML 's finetune step . fewer overall parameters compared to matching networks and the meta - learner LSTM , since the algorithm does not introduce any additional parameters beyond the weights of the classifier itself . Compared to these prior methods , memory - augmented neural networks [ reference ] specifically , and recurrent meta - learning models in general , represent a more broadly applicable class of methods that , like MAML , can be used for other tasks such as reinforcement learning [ reference ][ reference ] . However , as shown in the comparison , MAML significantly outperforms memory - augmented networks and the meta - learner LSTM on 5 - way Omniglot and MiniImagenet classification , both in the 1 - shot and 5 - shot case . The selected knowledge base , called the Agent Random-Access Meta- Structure ( ARAMS ) , is shown to support MAML and operate in a distributed environment that permits sharing of agent development knowledge between various tools and tool instances . Tests of MAML and ARAMS with agentTool are summarized , and related future work suggested . First , pick any modern object detector trained with gradient descent . Second , conduct offline training ( or initialization ) with MAML . Third , perform domain adaptation using the initial frame . We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding . Inspired by the Model-Agnostic Meta-Learning framework ( MAML ) , we introduce the Attentive Task-Agnostic Meta-Learning ( ATAML ) algorithm for text classification . The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms . To empower non-technical users to participate in the creation of supportive apps , graphical domain-specific languages can be used . Consequently , we propose the Munster App Modeling Language ( MAML ) to specify business apps through graphical building blocks on a high level of abstraction . In contrast to existing process modelling notations , these models can directly be transformed into apps for multiple platforms across different device classes through code generators without the need for manual programming . For knowledge triplets embedding and selection , we formulate it as a problem of sentence embedding to better capture semantic information . Our improved MAML algorithm is capable of learning general features from a limited number of knowledge graphs , which can also quickly adapt to dialogue generation with unseen knowledge triplets . Extensive experiments are conducted on a knowledge aware dialogue dataset ( DuConv ) . In this work , we propose a different perspective and view this diversity as an inductive transfer learning problem where few data samples from one region allow a model to adapt to an unseen region . We evaluate the modelagnostic meta-learning ( MAML ) algorithm on classification and segmentation tasks using globally and regionally distributed datasets . We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on the ( 1 ) Sen12MS dataset and ( 2 ) DeepGlobe dataset when the source domain and target domain differ . Recent advances such as textual domain-specific languages ( DSL ) for cross-platform app generation reduce development efforts , but still focus on technical users . To alleviate these problems , the Münster App Modeling Language ( MAML ) is proposed as novel graphical DSL for specifying business apps . For each task to be accomplished within the app , the abstract process flows are modelled together with the respective data elements and view specifications in a combined model . Accordingly , pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots . Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated , including Model-Agnostic Meta-Learning ( MAML ) , First-Order MAML ( FOMAML ) , REPTILE , and fast Context Adaptation VIA meta-learning ( CAVIA ) . Both offline and online solutions are developed . In this paper , we augment MAML with the capability to identify the mode of tasks sampled from a multimodal task distribution and adapt quickly through gradient updates . Specifically , we propose a multimodal MAML ( MMAML ) framework , which is able to modulate its meta-learned prior parameters according to the identified mode , allowing more efficient fast adaptation . We evaluate the proposed model on a diverse set of few-shot learning tasks , including regression , image classification , and reinforcement learning . Model-agnostic meta-learning ( MAML ) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks . The MAML algorithm performs well on few-shot learning problems in classification , regression , and fine-tuning of policy gradients in reinforcement learning , but comes with the need for costly hyperparameter tuning for training stability . Extensive experiments on the Miniimagenet and Omniglot data sets demonstrate that HIDRA improves over standard approaches while generalizing to tasks with any number of target variables . Moreover , our approach is shown to robustify low-capacity models in learning across complex tasks with a high number of classes for which regular MAML fails to learn any feasible initialization . The full algorithm , in the general case , is outlined in Algorithm 1 . The MAML meta - gradient update involves a gradient through a gradient . Computationally , this requires an additional backward pass through f to compute Hessian - vector products , which is supported by standard deep learning libraries such as TensorFlow The network was evaluated using 3 gradient steps with the same step size α = 0.4 . The 20 - way convolutional MAML model was trained and evaluated with 5 gradient steps with step size α = 0.1 . During training , the meta batch - size was set to 16 tasks . Learning to Fine - Tune The successful MAML approach aimed to meta - learn an initial condition ( set of neural network weights ) that is good for fine - tuning on few - shot problems . The strategy here is to search for the weight configuration of a given neural network such that it can be effectively fine - tuned on a sparse data problem within a few gradient - descent update steps . All our models are end - to - end trained from scratch with no additional dataset . Baselines We compare against various state of the art baselines for few - shot recognition , including neural statistician , Matching Nets with and without fine - tuning , MANN , Siamese Nets with Memory , Convolutional Siamese Nets , MAML , Meta Nets , Prototypical Nets and Meta - Learner LSTM . subsubsection : Omniglot Meta - learning aims to accumulate experience from learning multiple tasks , while base - learning focuses on modeling the data distribution of a single task . A state - of - the - art representative of this , namely Model - Agnostic Meta - Learning ( MAML ) , learns to search for the optimal initialization state to fast adapt a base - learner to a new task . Its task - agnostic property makes it possible to generalize to few - shot supervised learning as well as unsupervised reinforcement learning . ) Gradient descent based methods have a specific meta - learner that learns to adapt a specific base - learner ( to few - shot examples ) through different tasks . E.g. MAML uses a meta - learner that learns to effectively initialize a base - learner for a new learning task . Meta - learner optimization is done by gradient descent using the validation loss of the base - learner . subsection : Ablation study setting In order to show the effectiveness of our approach , we design some ablative settings : two baselines without meta - learning but more classic learning , three baselines of Fine - Tuning ( FT ) on smaller number of parameters ( Table [ reference ] ) , and two MAML variants using our deeper pre - trained model and HT meta - batch ( Table [ reference ] and Table [ reference ] ) . Note that the alternative meta - learning operation to SS is the FT used in MAML . Compared to MAML variants , our MTL results are consistently higher , e.g. on FC100 . People may argue that MAML fine - tuning ( FT ) all network parameters is likely to overfit to few - shot data . As introduced in § [ reference ] , we use the same setting as MAML which used a smaller number of samples for episode test ( sample for 1 - shot episode test and samples for 5 - shot ) , making the result variance higher . Other works that used more samples for episode test got lower variances , , TADAM used samples and its variances are about and of MAML ’s respectively for miniImageNet 1 - shot and 5 - shot . In order to have a fair comparison with TADAM in terms of this issue , we supplement the experiments using episode test samples at the meta - test . In this paper , we propose a meta-learning based generative model that naturally handles these limitations . The proposed model is based on integrating model-agnostic meta learning with a Wasserstein GAN ( WGAN ) to handle ( i ) and ( iii ) , and uses a novel task distribution to handle ( ii ) . Our proposed model yields significant improvements on standard ZSL as well as more challenging GZSL setting . The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model . Recent works show that there exist weight initializations from which optimization procedures can find the task-specific parameters faster than from uniformly random initializations and that such a weight initialization can be learned by optimizing a specific model architecture across similar tasks via MAML ( Model-Agnostic Meta-Learning ) . Current methods are limited to populations of classification tasks that share the same number of classes due to the static model architectures used during meta-learning . We also provide matching lower bounds of BSGD for convex CSO objectives . Extensive numerical experiments are conducted to illustrate the performance of BSGD on robust logistic regression , model-agnostic meta-learning ( MAML ) , and instrumental variable regression ( IV ) . We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding . Inspired by the Model-Agnostic Meta-Learning framework ( MAML ) , we introduce the Attentive Task-Agnostic Meta-Learning ( ATAML ) algorithm for text classification . The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms . Meta-learning is also named as learning to learn . In this study , we propose a LSTM-model based meta-learning framework for the histopathology image classification . We apply the DoubleOpponent ( DO ) neurons to model the texture patterns of histopathology images . In this paper , we propose a domain-invariant projection to improve the generalizability of speaker vectors . This projection is a simple neural net and is trained following the Model-Agnostic Meta-Learning ( MAML ) principle , for which the objective is to classify speakers in one domain if it had been updated with speech data in another domain . We tested the proposed method on CNCeleb , a new dataset consisting of single-speaker multi-condition ( SSMC ) data . We propose a meta-relation network to solve the few shot learning problem , where the classifier must learn to recognize new classes given only few examples from each . Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning ( MAML ) training methods , which can be trained end-to-end . After training with MAML algorithm , the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step . Our approach naturally combines a retrieval model and a meta-learner , where the former learns to find similar datapoints from the training data , and the latter considers retrieved datapoints as a pseudo task for fast adaptation . Specifically , our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration , and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation . We conduct experiments on CONCODE and CSQA datasets , where the context refers to class environment in JAVA codes and conversational history , respectively . For this formulation , we propose a variant of the MAML method , named Stochastic Gradient Meta-Reinforcement Learning ( SG-MRL ) , and study its convergence properties . We derive the iteration and sample complexity of SG-MRL to find an $ \epsilon$-first-order stationary point , which , to the best of our knowledge , provides the first convergence guarantee for model-agnostic meta-reinforcement learning algorithms . We further show how our results extend to the case where more than one step of stochastic policy gradient method is used in the update during the test time . Experimental simulations on various benchmark datasets show that the recursive operation for the hidden RBF response units is promising . Comparisons with the two standard model meta-learning architectures , namely Committee Machines and Cascaded Machines , reveal that the proposed method produces similar results and compares well as a combiner that can merge many HiMarkovRBFNN child nodes into one higher level parent HiMarkovRBFNN node of the same functionality . In this paper , we propose a probabilistic meta - learning algorithm that can sample models for a new task from a model distribution . Our approach extends model - agnostic meta - learning , which adapts to new tasks via gradient descent , to incorporate a parameter distribution that is trained via a variational lower bound . At meta - test time , our algorithm adapts via a simple procedure that injects noise into gradient descent , and at meta - training time , the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior . Figure 1 . Diagram of our model - agnostic meta - learning algorithm ( MAML ) , which optimizes for a representation θ that can quickly adapt to new tasks . In our meta - learning scenario , we consider a distribution over tasks p ( T ) that we want our model to be able to adapt to . Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class . Recent advances benefit from the meta-learning process where episodic tasks are formed to train a model that can adapt to class change . However , these task sare independent to each other and existing works mainly rely on limited samples of individual support set in a single meta task . Few-shot image classification aims to classify unseen classes with limited labeled samples . Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing . Due to the limited number of samples for each task , the initial embedding network for meta learning becomes an essential component and can largely affects the performance in practice . Yet , the key challenge of how to learn a generalizable classifier with the capability of adapting to specific tasks with severely limited data still remains in this domain . To this end , we propose a Transductive Episodic-wise Adaptive Metric ( TEAM ) framework for few-shot learning , by integrating the meta-learning paradigm with both deep metric learning and transductive inference . With exploring the pairwise constraints and regularization prior within each task , we explicitly formulate the adaptation procedure into a standard semi-definite programming problem . Specifically , our proposed model is designed as a spatial-temporal network with a meta-learning paradigm . The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network , which can be effectively adapted to target cities . In addition , a pattern-based spatial-temporal memory is designed to distill long-term temporal information ( i.e. , periodicity ) . This paper proposes an efficient domain adaption approach using deep learning along with transfer and meta-level learning . The objective is to identify how many blocks ( i.e. groups of consecutive layers ) of a pre-trained image classification network need to be fine-tuned based on the characteristics of the new task . Learning occurs at different levels , learning at the meta-level and learning at the level of the specific problem solvers . Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks . Meta-level tasks include the initial planning of problem solving strategies , and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving . When compared to the base methods , however , the proposed solution presented minor difference of performance . These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP . The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity , compared to the baseline policy gradient algorithm . Simulation results show that , the proposed meta-learning solution yields a 25 % improvement in the convergence speed , and about 10 % improvement in the DBS ' communication performance , compared to a baseline policy gradient algorithm . Meanwhile , the probability that the DBS serves over 50 % of user requests increases about 27 % , compared to the baseline policy gradient algorithm . In this paper , we describe an alternative approach to resampling selection . We follow meta-learning concept to build resampling recommendation systems , i.e. , algorithms recommending resampling for datasets on the basis of their properties . Since the mapping from context to behavior is often complex , we take a learning reduction approach to reduce smooth imitation learning to a regression problem using complex function classes that are regularized to ensure smoothness . We present a learning meta-algorithm that achieves fast and stable convergence to a good policy . Our approach enjoys several attractive properties , including being fully deterministic , employing an adaptive learning rate that can provably yield larger policy improvements compared to previous approaches , and the ability to ensure stable convergence . For this setting , we propose a performance measure called exploration steps which counts the time steps at which the learner lacks sufficient knowledge to navigate its environment efficiently . We devise a learning meta-algorithm , MNM and prove an upper bound on the exploration steps in terms of the number of changes . The section IV introduces OWL-DL ontology model of data-mining MAS and its different components , i.e. its role-based model , model of data-mining methods , and model of metadata . In section V the meta learning scenario is realized by means querying of this OWL-DL model . Section VI concludes the paper . The strong adaptation power of MetaFuse is due in large part to the proposed factorization of the original fusion model into two parts—(1 ) a generic fusion model shared by all cameras , and ( 2 ) lightweight camera-dependent transformations . Furthermore , the generic model is learned from many cameras by a meta-learning style algorithm to maximize its adaptation capability to various camera poses . We observe in experiments that MetaFuse finetuned on the public datasets outperforms the state-of-the-arts by a large margin which validates its value in practice . Such visual cues include object-object interactions , handgrasps and motion within regions that are a function of handlocations . We suggest a framework based on meta-learningto appropriately extract the distinctive and domain invari-ant components of the deployed visual cues , so to be able totransfer action classification models across public datasetscaptured with different scene configurations . We thoroughlyevaluate our methodology and report promising results overstate-of-the-art action classification approaches for bothinter-class and inter-dataset transfer . Our method , called the Relation Network ( RN ) , is trained end - to - end from scratch . During meta - learning , it learns to learn a deep distance metric to compare a small number of images within episodes , each of which is designed to simulate the few - shot setting . Once trained , a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network . Data augmentation and regularisation techniques can alleviate overfitting in such a limited - data regime , but they do not solve it . Therefore contemporary approaches to few - shot learning often decompose training into an auxiliary meta learning phase where transferrable knowledge is learned in the form of good initial conditions , embeddings or optimisation strategies . The target few - shot learning problem is then learned by fine - tuning with the learned optimisation strategy or computed in a feed - forward pass without updating network weights . With the success of discriminative deep learning - based approaches in the data - rich many - shot setting , there has been a surge of interest in generalising such deep learning approaches to the few - shot learning setting . Many of these approaches use a meta - learning or learning - to - learn strategy in the sense that they extract some transferrable knowledge from a set of auxiliary tasks ( meta - learning , learning - to - learn ) , which then helps them to learn the target few - shot problem well without suffering from the overfitting that might be expected when applying deep models to sparse data problems . Learning to Fine - Tune document : Meta - Transfer Learning for Few - Shot Learning Meta - learning has been proposed as a framework to address the challenging few - shot learning setting . As deep neural networks ( DNNs ) tend to overfit using a few samples only , meta - learning typically uses shallow neural networks ( SNNs ) , thus limiting its effectiveness . In this paper we propose a novel few - shot learning method called meta - transfer learning ( MTL ) which learns to adapt a deep NN for few shot learning tasks . Specifically , meta refers to training multiple tasks , and transfer is achieved by learning scaling and shifting functions of DNN weights for each task . The learned classifier will be discarded , because subsequent few - shot tasks contain different classification objectives , e.g. - class instead of - class classification for miniImageNet . subsection : Meta - transfer learning ( MTL ) As shown in Figure [ reference ] ( b ) , our proposed meta - transfer learning ( MTL ) method optimizes the meta operations Scaling and Shifting ( SS ) through HT meta - batch training ( Section [ reference ] ) . In order to show the effectiveness of our approach , we design some ablative settings : two baselines without meta - learning but more classic learning , three baselines of Fine - Tuning ( FT ) on smaller number of parameters ( Table [ reference ] ) , and two MAML variants using our deeper pre - trained model and HT meta - batch ( Table [ reference ] and Table [ reference ] ) . Note that the alternative meta - learning operation to SS is the FT used in MAML . Some bullet names are explained as follows . the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification . In meta - learning , the goal of the trained model is to quickly learn a new task from a small amount of new data , and the model is trained by the meta - learner to be able to learn on a large number of different tasks . The key idea underlying our method is to train the model 's initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task . The goal of few - shot meta - learning is to train a model that can quickly adapt to a new task using only a few datapoints and training iterations . To accomplish this , the model or learner is trained during a meta - learning phase on a set of tasks , such that the trained model can quickly adapt to new tasks using only a small number of examples or trials . In effect , the meta - learning problem treats entire tasks as training examples . T i serves as the training error of the meta - learning process . At the end of meta - training , new tasks are sampled from p ( T ) , and meta - performance is measured by the model 's performance after learning from K samples . While these approaches have been demonstrated on simple few - shot image classification datasets [ reference ] , they have yet to scale to the more complex problems , such as the experiments in this paper . A number of works have approached the problem of few - shot learning from a meta - learning perspective [ reference ][ reference ] , including black - box [ reference ][ reference ][ reference ] and optimization - based approaches [ reference ][ reference ] . While these approaches scale to large - scale image datasets [ reference ] and visual reinforcement learning problems [ reference ] , they typically lack the ability to reason about uncertainty . Our work is most related to methods that combine deep networks and probabilistic methods for few - shot learning The key assumption of learning - to - learn is that the tasks in this distribution share common structure that can be exploited for faster learning of new tasks . Thus , the goal of the meta - learning process is to discover that structure . In this section , we will introduce notation and overview the model - agnostic meta - learning ( MAML ) algorithm [ reference ] . We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories , followed by a mode seeking KL divergence to train a robust policy . We propose Meta Reward Learning ( MeRL ) to construct an auxiliary reward function that provides more refined feedback for learning . The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy . The MeRL approach outperforms our alternative reward learning technique based on Bayesian Optimization , and achieves the state-of-the-art on weakly-supervised semantic parsing . It improves previous work by 1.2 % and 2.4 % on WikiTableQuestions and WikiSQL datasets respectively .
Based on this hypothesis , we propose an architecture for knowledge-based instance selection ( KBIS ) systems . It uses meta-learning to select the best suited instance selection method for each specific database , among several methods available . We carried out a study in order to verify whether this architecture can outperform the individual methods .
Unlike the vanilla MixUp that samples interpolation policy from a predefined distribution , this paper introduces a meta-learning based online optimization approach to dynamically learn the interpolation policy in a data-adaptive way . The validation set performance via meta-learning captures the underfitting issue , which provides more information to refine interpolation policy . Furthermore , we adapt our method for pseudo-label based semisupervised learning ( SSL ) along with a refined pseudo-labeling strategy .
As there are several learning algorithms available , choosing one whose bias suits the current data best is not a trivial task . In this paper , we present a meta-learning based method for periodic algorithm selection in time-changing environments , named MetaStream . It works by mapping the characteristics extracted from the past and incoming data to the performance of regression models in order to choose between single learning algorithms or their combination .
Meta-Reinforcement learning approaches aim to develop learning procedures that can adapt quickly to a distribution of tasks with the help of a few examples . Developing efficient exploration strategies capable of finding the most useful samples becomes critical in such settings .
State-of-the-art meta reinforcement learning algorithms typically assume the setting of a single agent interacting with its environment in a sequential manner . A negative side-effect of this sequential execution paradigm is that , as the environment becomes more and more challenging , and thus requiring more interaction episodes for the meta-learner , it needs the agent to reason over longer and longer time-scales .
For part 3 ( classification ) , the submitted test run as well as our last official validation run were the result from a meta-model that assembled seven base deep-learning models : three based on Inception-V4 trained on our largest dataset ; three based on Inception trained on our smallest dataset ; and one based on ResNet-101 trained on our smaller dataset . The results of those component models were stacked in a meta-learning layer based on an SVM trained on the validation set of our largest dataset .
Both batch and online annotation settings are experimented with . It is observed that the addition of this meta-learning layer produces much improved results that outperform best-known results . For the online setting , the T/T approach produces progressively better annotation with time , significantly outperforming the black-box as well as the static form of the meta-learner , on real-world data .
In this paper , we show what types of incentive systems of rewards and punishments promote and maintain effective information behaviors or cooperative regimes in actual groupware . Our agent-based simulation demonstrates that a meta-reward system in which rewarders can gain other benefits for their own reward actions will probably encourage cooperation . Counterintuitively , our simulation also demonstrates that a system that applies sanctioning functions does not necessarily promote cooperation .
We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories , followed by a mode seeking KL divergence to train a robust policy . We propose Meta Reward Learning ( MeRL ) to construct an auxiliary reward function that provides more refined feedback for learning . The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy .
To this end , we develop a novel meta reinforcement learning framework , Hyper-Meta RL ( HMRL ) , for sparse reward RL problems . It consists of meta state embedding , meta reward shaping and meta policy learning modules : The cross-environment meta state embedding module constructs a common meta state space to adapt to different environments ; The meta state based environment-specific meta reward shaping effectively extends the original sparse reward trajectory by cross-environmental knowledge complementarity ; As a consequence , the meta policy then achieves better generalization and efficiency with the shaped meta reward .
However , in many scenarios this reward signal might not be readily available for the target task , or the difference between the environments can be implicit and only observable from the dynamics . To this end , we introduce a method that allows for self-adaptation of learned policies : No-Reward Meta Learning ( NoRML ) . NoRML extends Model Agnostic Meta Learning ( MAML ) for RL and uses observable dynamics of the environment instead of an explicit reward function in MAML 's finetune step .
fewer overall parameters compared to matching networks and the meta - learner LSTM , since the algorithm does not introduce any additional parameters beyond the weights of the classifier itself . Compared to these prior methods , memory - augmented neural networks [ reference ] specifically , and recurrent meta - learning models in general , represent a more broadly applicable class of methods that , like MAML , can be used for other tasks such as reinforcement learning [ reference ][ reference ] . However , as shown in the comparison , MAML significantly outperforms memory - augmented networks and the meta - learner LSTM on 5 - way Omniglot and MiniImagenet classification , both in the 1 - shot and 5 - shot case .
The selected knowledge base , called the Agent Random-Access Meta- Structure ( ARAMS ) , is shown to support MAML and operate in a distributed environment that permits sharing of agent development knowledge between various tools and tool instances . Tests of MAML and ARAMS with agentTool are summarized , and related future work suggested .
First , pick any modern object detector trained with gradient descent . Second , conduct offline training ( or initialization ) with MAML . Third , perform domain adaptation using the initial frame .
We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding . Inspired by the Model-Agnostic Meta-Learning framework ( MAML ) , we introduce the Attentive Task-Agnostic Meta-Learning ( ATAML ) algorithm for text classification . The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms .
To empower non-technical users to participate in the creation of supportive apps , graphical domain-specific languages can be used . Consequently , we propose the Munster App Modeling Language ( MAML ) to specify business apps through graphical building blocks on a high level of abstraction . In contrast to existing process modelling notations , these models can directly be transformed into apps for multiple platforms across different device classes through code generators without the need for manual programming .
For knowledge triplets embedding and selection , we formulate it as a problem of sentence embedding to better capture semantic information . Our improved MAML algorithm is capable of learning general features from a limited number of knowledge graphs , which can also quickly adapt to dialogue generation with unseen knowledge triplets . Extensive experiments are conducted on a knowledge aware dialogue dataset ( DuConv ) .
In this work , we propose a different perspective and view this diversity as an inductive transfer learning problem where few data samples from one region allow a model to adapt to an unseen region . We evaluate the modelagnostic meta-learning ( MAML ) algorithm on classification and segmentation tasks using globally and regionally distributed datasets . We find that few-shot model adaptation outperforms pre-training with regular gradient descent and fine-tuning on the ( 1 ) Sen12MS dataset and ( 2 ) DeepGlobe dataset when the source domain and target domain differ .
Recent advances such as textual domain-specific languages ( DSL ) for cross-platform app generation reduce development efforts , but still focus on technical users . To alleviate these problems , the Münster App Modeling Language ( MAML ) is proposed as novel graphical DSL for specifying business apps . For each task to be accomplished within the app , the abstract process flows are modelled together with the respective data elements and view specifications in a combined model .
Accordingly , pilots from previous IoT transmissions are used as meta-training data in order to train a demodulator that is able to quickly adapt to new end-to-end channel conditions from few pilots . Various state-of-the-art meta-learning schemes are adapted to the problem at hand and evaluated , including Model-Agnostic Meta-Learning ( MAML ) , First-Order MAML ( FOMAML ) , REPTILE , and fast Context Adaptation VIA meta-learning ( CAVIA ) . Both offline and online solutions are developed .
In this paper , we augment MAML with the capability to identify the mode of tasks sampled from a multimodal task distribution and adapt quickly through gradient updates . Specifically , we propose a multimodal MAML ( MMAML ) framework , which is able to modulate its meta-learned prior parameters according to the identified mode , allowing more efficient fast adaptation . We evaluate the proposed model on a diverse set of few-shot learning tasks , including regression , image classification , and reinforcement learning .
Model-agnostic meta-learning ( MAML ) is a meta-learning technique to train a model on a multitude of learning tasks in a way that primes the model for few-shot learning of new tasks . The MAML algorithm performs well on few-shot learning problems in classification , regression , and fine-tuning of policy gradients in reinforcement learning , but comes with the need for costly hyperparameter tuning for training stability .
Extensive experiments on the Miniimagenet and Omniglot data sets demonstrate that HIDRA improves over standard approaches while generalizing to tasks with any number of target variables . Moreover , our approach is shown to robustify low-capacity models in learning across complex tasks with a high number of classes for which regular MAML fails to learn any feasible initialization .
The full algorithm , in the general case , is outlined in Algorithm 1 . The MAML meta - gradient update involves a gradient through a gradient . Computationally , this requires an additional backward pass through f to compute Hessian - vector products , which is supported by standard deep learning libraries such as TensorFlow
The network was evaluated using 3 gradient steps with the same step size α = 0.4 . The 20 - way convolutional MAML model was trained and evaluated with 5 gradient steps with step size α = 0.1 . During training , the meta batch - size was set to 16 tasks .
Learning to Fine - Tune The successful MAML approach aimed to meta - learn an initial condition ( set of neural network weights ) that is good for fine - tuning on few - shot problems . The strategy here is to search for the weight configuration of a given neural network such that it can be effectively fine - tuned on a sparse data problem within a few gradient - descent update steps .
All our models are end - to - end trained from scratch with no additional dataset . Baselines We compare against various state of the art baselines for few - shot recognition , including neural statistician , Matching Nets with and without fine - tuning , MANN , Siamese Nets with Memory , Convolutional Siamese Nets , MAML , Meta Nets , Prototypical Nets and Meta - Learner LSTM . subsubsection : Omniglot
Meta - learning aims to accumulate experience from learning multiple tasks , while base - learning focuses on modeling the data distribution of a single task . A state - of - the - art representative of this , namely Model - Agnostic Meta - Learning ( MAML ) , learns to search for the optimal initialization state to fast adapt a base - learner to a new task . Its task - agnostic property makes it possible to generalize to few - shot supervised learning as well as unsupervised reinforcement learning .
) Gradient descent based methods have a specific meta - learner that learns to adapt a specific base - learner ( to few - shot examples ) through different tasks . E.g. MAML uses a meta - learner that learns to effectively initialize a base - learner for a new learning task . Meta - learner optimization is done by gradient descent using the validation loss of the base - learner .
subsection : Ablation study setting In order to show the effectiveness of our approach , we design some ablative settings : two baselines without meta - learning but more classic learning , three baselines of Fine - Tuning ( FT ) on smaller number of parameters ( Table [ reference ] ) , and two MAML variants using our deeper pre - trained model and HT meta - batch ( Table [ reference ] and Table [ reference ] ) . Note that the alternative meta - learning operation to SS is the FT used in MAML .
Compared to MAML variants , our MTL results are consistently higher , e.g. on FC100 . People may argue that MAML fine - tuning ( FT ) all network parameters is likely to overfit to few - shot data .
As introduced in § [ reference ] , we use the same setting as MAML which used a smaller number of samples for episode test ( sample for 1 - shot episode test and samples for 5 - shot ) , making the result variance higher . Other works that used more samples for episode test got lower variances , , TADAM used samples and its variances are about and of MAML ’s respectively for miniImageNet 1 - shot and 5 - shot . In order to have a fair comparison with TADAM in terms of this issue , we supplement the experiments using episode test samples at the meta - test .
In this paper , we propose a meta-learning based generative model that naturally handles these limitations . The proposed model is based on integrating model-agnostic meta learning with a Wasserstein GAN ( WGAN ) to handle ( i ) and ( iii ) , and uses a novel task distribution to handle ( ii ) . Our proposed model yields significant improvements on standard ZSL as well as more challenging GZSL setting .
The performance of gradient-based optimization strategies depends heavily on the initial weights of the parametric model . Recent works show that there exist weight initializations from which optimization procedures can find the task-specific parameters faster than from uniformly random initializations and that such a weight initialization can be learned by optimizing a specific model architecture across similar tasks via MAML ( Model-Agnostic Meta-Learning ) . Current methods are limited to populations of classification tasks that share the same number of classes due to the static model architectures used during meta-learning .
We also provide matching lower bounds of BSGD for convex CSO objectives . Extensive numerical experiments are conducted to illustrate the performance of BSGD on robust logistic regression , model-agnostic meta-learning ( MAML ) , and instrumental variable regression ( IV ) .
We address this problem by integrating a meta-learning procedure that uses the knowledge learned across many tasks as an inductive bias towards better natural language understanding . Inspired by the Model-Agnostic Meta-Learning framework ( MAML ) , we introduce the Attentive Task-Agnostic Meta-Learning ( ATAML ) algorithm for text classification . The proposed ATAML is designed to encourage task-agnostic representation learning by way of task-agnostic parameterization and facilitate task-specific adaptation via attention mechanisms .
Meta-learning is also named as learning to learn . In this study , we propose a LSTM-model based meta-learning framework for the histopathology image classification . We apply the DoubleOpponent ( DO ) neurons to model the texture patterns of histopathology images .
In this paper , we propose a domain-invariant projection to improve the generalizability of speaker vectors . This projection is a simple neural net and is trained following the Model-Agnostic Meta-Learning ( MAML ) principle , for which the objective is to classify speakers in one domain if it had been updated with speech data in another domain . We tested the proposed method on CNCeleb , a new dataset consisting of single-speaker multi-condition ( SSMC ) data .
We propose a meta-relation network to solve the few shot learning problem , where the classifier must learn to recognize new classes given only few examples from each . Meta-relation networks is based on relation networks and Model-Agnostic Meta-Learning ( MAML ) training methods , which can be trained end-to-end . After training with MAML algorithm , the meta-relation networks can adapt to learning quickly for a small number of samples from a new task with only a small amount of gradient step .
Our approach naturally combines a retrieval model and a meta-learner , where the former learns to find similar datapoints from the training data , and the latter considers retrieved datapoints as a pseudo task for fast adaptation . Specifically , our retriever is a context-aware encoder-decoder model with a latent variable which takes context environment into consideration , and our meta-learner learns to utilize retrieved datapoints in a model-agnostic meta-learning paradigm for fast adaptation . We conduct experiments on CONCODE and CSQA datasets , where the context refers to class environment in JAVA codes and conversational history , respectively .
For this formulation , we propose a variant of the MAML method , named Stochastic Gradient Meta-Reinforcement Learning ( SG-MRL ) , and study its convergence properties . We derive the iteration and sample complexity of SG-MRL to find an $ \epsilon$-first-order stationary point , which , to the best of our knowledge , provides the first convergence guarantee for model-agnostic meta-reinforcement learning algorithms . We further show how our results extend to the case where more than one step of stochastic policy gradient method is used in the update during the test time .
Experimental simulations on various benchmark datasets show that the recursive operation for the hidden RBF response units is promising . Comparisons with the two standard model meta-learning architectures , namely Committee Machines and Cascaded Machines , reveal that the proposed method produces similar results and compares well as a combiner that can merge many HiMarkovRBFNN child nodes into one higher level parent HiMarkovRBFNN node of the same functionality .
In this paper , we propose a probabilistic meta - learning algorithm that can sample models for a new task from a model distribution . Our approach extends model - agnostic meta - learning , which adapts to new tasks via gradient descent , to incorporate a parameter distribution that is trained via a variational lower bound . At meta - test time , our algorithm adapts via a simple procedure that injects noise into gradient descent , and at meta - training time , the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior .
Figure 1 . Diagram of our model - agnostic meta - learning algorithm ( MAML ) , which optimizes for a representation θ that can quickly adapt to new tasks . In our meta - learning scenario , we consider a distribution over tasks p ( T ) that we want our model to be able to adapt to .
Few-shot image classification requires the classifier to robustly cope with unseen classes even if there are only a few samples for each class . Recent advances benefit from the meta-learning process where episodic tasks are formed to train a model that can adapt to class change . However , these task sare independent to each other and existing works mainly rely on limited samples of individual support set in a single meta task .
Few-shot image classification aims to classify unseen classes with limited labeled samples . Recent works benefit from the meta-learning process with episodic tasks and can fast adapt to class from training to testing . Due to the limited number of samples for each task , the initial embedding network for meta learning becomes an essential component and can largely affects the performance in practice .
Yet , the key challenge of how to learn a generalizable classifier with the capability of adapting to specific tasks with severely limited data still remains in this domain . To this end , we propose a Transductive Episodic-wise Adaptive Metric ( TEAM ) framework for few-shot learning , by integrating the meta-learning paradigm with both deep metric learning and transductive inference . With exploring the pairwise constraints and regularization prior within each task , we explicitly formulate the adaptation procedure into a standard semi-definite programming problem .
Specifically , our proposed model is designed as a spatial-temporal network with a meta-learning paradigm . The meta-learning paradigm learns a well-generalized initialization of the spatial-temporal network , which can be effectively adapted to target cities . In addition , a pattern-based spatial-temporal memory is designed to distill long-term temporal information ( i.e. , periodicity ) .
This paper proposes an efficient domain adaption approach using deep learning along with transfer and meta-level learning . The objective is to identify how many blocks ( i.e. groups of consecutive layers ) of a pre-trained image classification network need to be fine-tuned based on the characteristics of the new task .
Learning occurs at different levels , learning at the meta-level and learning at the level of the specific problem solvers . Meta-level learning reflects the ability of the metareasoner to improve the overall performance of the hybrid system by improving the efficiency of meta-level tasks . Meta-level tasks include the initial planning of problem solving strategies , and the dynamic adaptation of chosen strategies depending on new events occurring dynamically during problem solving .
When compared to the base methods , however , the proposed solution presented minor difference of performance . These results provide valuable knowledge about the possibilities and limitations of a meta-learning solution applied for CPDP .
The meta-tuned RL is shown to yield a faster convergence to the optimal coverage in unseen environments with a considerably low computation complexity , compared to the baseline policy gradient algorithm . Simulation results show that , the proposed meta-learning solution yields a 25 % improvement in the convergence speed , and about 10 % improvement in the DBS ' communication performance , compared to a baseline policy gradient algorithm . Meanwhile , the probability that the DBS serves over 50 % of user requests increases about 27 % , compared to the baseline policy gradient algorithm .
In this paper , we describe an alternative approach to resampling selection . We follow meta-learning concept to build resampling recommendation systems , i.e. , algorithms recommending resampling for datasets on the basis of their properties .
Since the mapping from context to behavior is often complex , we take a learning reduction approach to reduce smooth imitation learning to a regression problem using complex function classes that are regularized to ensure smoothness . We present a learning meta-algorithm that achieves fast and stable convergence to a good policy . Our approach enjoys several attractive properties , including being fully deterministic , employing an adaptive learning rate that can provably yield larger policy improvements compared to previous approaches , and the ability to ensure stable convergence .
For this setting , we propose a performance measure called exploration steps which counts the time steps at which the learner lacks sufficient knowledge to navigate its environment efficiently . We devise a learning meta-algorithm , MNM and prove an upper bound on the exploration steps in terms of the number of changes .
The section IV introduces OWL-DL ontology model of data-mining MAS and its different components , i.e. its role-based model , model of data-mining methods , and model of metadata . In section V the meta learning scenario is realized by means querying of this OWL-DL model . Section VI concludes the paper .
The strong adaptation power of MetaFuse is due in large part to the proposed factorization of the original fusion model into two parts—(1 ) a generic fusion model shared by all cameras , and ( 2 ) lightweight camera-dependent transformations . Furthermore , the generic model is learned from many cameras by a meta-learning style algorithm to maximize its adaptation capability to various camera poses . We observe in experiments that MetaFuse finetuned on the public datasets outperforms the state-of-the-arts by a large margin which validates its value in practice .
Such visual cues include object-object interactions , handgrasps and motion within regions that are a function of handlocations . We suggest a framework based on meta-learningto appropriately extract the distinctive and domain invari-ant components of the deployed visual cues , so to be able totransfer action classification models across public datasetscaptured with different scene configurations . We thoroughlyevaluate our methodology and report promising results overstate-of-the-art action classification approaches for bothinter-class and inter-dataset transfer .
Our method , called the Relation Network ( RN ) , is trained end - to - end from scratch . During meta - learning , it learns to learn a deep distance metric to compare a small number of images within episodes , each of which is designed to simulate the few - shot setting . Once trained , a RN is able to classify images of new classes by computing relation scores between query images and the few examples of each new class without further updating the network .
Data augmentation and regularisation techniques can alleviate overfitting in such a limited - data regime , but they do not solve it . Therefore contemporary approaches to few - shot learning often decompose training into an auxiliary meta learning phase where transferrable knowledge is learned in the form of good initial conditions , embeddings or optimisation strategies . The target few - shot learning problem is then learned by fine - tuning with the learned optimisation strategy or computed in a feed - forward pass without updating network weights .
With the success of discriminative deep learning - based approaches in the data - rich many - shot setting , there has been a surge of interest in generalising such deep learning approaches to the few - shot learning setting . Many of these approaches use a meta - learning or learning - to - learn strategy in the sense that they extract some transferrable knowledge from a set of auxiliary tasks ( meta - learning , learning - to - learn ) , which then helps them to learn the target few - shot problem well without suffering from the overfitting that might be expected when applying deep models to sparse data problems . Learning to Fine - Tune
document : Meta - Transfer Learning for Few - Shot Learning Meta - learning has been proposed as a framework to address the challenging few - shot learning setting .
As deep neural networks ( DNNs ) tend to overfit using a few samples only , meta - learning typically uses shallow neural networks ( SNNs ) , thus limiting its effectiveness . In this paper we propose a novel few - shot learning method called meta - transfer learning ( MTL ) which learns to adapt a deep NN for few shot learning tasks . Specifically , meta refers to training multiple tasks , and transfer is achieved by learning scaling and shifting functions of DNN weights for each task .
The learned classifier will be discarded , because subsequent few - shot tasks contain different classification objectives , e.g. - class instead of - class classification for miniImageNet . subsection : Meta - transfer learning ( MTL ) As shown in Figure [ reference ] ( b ) , our proposed meta - transfer learning ( MTL ) method optimizes the meta operations Scaling and Shifting ( SS ) through HT meta - batch training ( Section [ reference ] ) .
In order to show the effectiveness of our approach , we design some ablative settings : two baselines without meta - learning but more classic learning , three baselines of Fine - Tuning ( FT ) on smaller number of parameters ( Table [ reference ] ) , and two MAML variants using our deeper pre - trained model and HT meta - batch ( Table [ reference ] and Table [ reference ] ) . Note that the alternative meta - learning operation to SS is the FT used in MAML . Some bullet names are explained as follows .
the form of computation required to complete the task . In this work , we propose a meta - learning algorithm that is general and model - agnostic , in the sense that it can be directly applied to any learning problem and model that is trained with a gradient descent procedure . Our focus is on deep neural network models , but we illustrate how our approach can easily handle different architectures and different problem settings , including classification , regression , and policy gradient reinforcement learning , with minimal modification . In meta - learning , the goal of the trained model is to quickly learn a new task from a small amount of new data , and the model is trained by the meta - learner to be able to learn on a large number of different tasks . The key idea underlying our method is to train the model 's initial parameters such that the model has maximal performance on a new task after the parameters have been updated through one or more gradient steps computed with a small amount of data from that new task .
The goal of few - shot meta - learning is to train a model that can quickly adapt to a new task using only a few datapoints and training iterations . To accomplish this , the model or learner is trained during a meta - learning phase on a set of tasks , such that the trained model can quickly adapt to new tasks using only a small number of examples or trials . In effect , the meta - learning problem treats entire tasks as training examples .
T i serves as the training error of the meta - learning process . At the end of meta - training , new tasks are sampled from p ( T ) , and meta - performance is measured by the model 's performance after learning from K samples .
While these approaches have been demonstrated on simple few - shot image classification datasets [ reference ] , they have yet to scale to the more complex problems , such as the experiments in this paper . A number of works have approached the problem of few - shot learning from a meta - learning perspective [ reference ][ reference ] , including black - box [ reference ][ reference ][ reference ] and optimization - based approaches [ reference ][ reference ] . While these approaches scale to large - scale image datasets [ reference ] and visual reinforcement learning problems [ reference ] , they typically lack the ability to reason about uncertainty . Our work is most related to methods that combine deep networks and probabilistic methods for few - shot learning
The key assumption of learning - to - learn is that the tasks in this distribution share common structure that can be exploited for faster learning of new tasks . Thus , the goal of the meta - learning process is to discover that structure . In this section , we will introduce notation and overview the model - agnostic meta - learning ( MAML ) algorithm [ reference ] .
We address exploration by using a mode covering direction of KL divergence to collect a diverse set of successful trajectories , followed by a mode seeking KL divergence to train a robust policy . We propose Meta Reward Learning ( MeRL ) to construct an auxiliary reward function that provides more refined feedback for learning . The parameters of the auxiliary reward function are optimized with respect to the validation performance of a trained policy . The MeRL approach outperforms our alternative reward learning technique based on Bayesian Optimization , and achieves the state-of-the-art on weakly-supervised semantic parsing . It improves previous work by 1.2 % and 2.4 % on WikiTableQuestions and WikiSQL datasets respectively .
On the use of meta-learning for instance selection: An architecture and an experimental study MetaMixUp: Learning Adaptive Interpolation Policy of MixUp with Meta-Learning MetaStream: A meta-learning based method for periodic algorithm selection in time-changing data MAME : Model-Agnostic Meta-Exploration Concurrent Meta Reinforcement Learning RECOD Titans at ISIC Challenge 2017 Tagging over time: real-world image annotation by lightweight meta-learning Exploring an Effective Incentive System on a Groupware Learning to Generalize from Sparse and Underspecified Rewards Hyper-Meta Reinforcement Learning with Sparse Reward NoRML: No-Reward Meta Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Knowledge Base Support for Design and Synthesis of Multi-Agent Systems Tracking by Instance Detection: A Meta-Learning Approach Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification Towards Pluri-Platform Development: Evaluating a Graphical Model-Driven Approach to App Development Across Device Classes Knowledge-graph based Proactive Dialogue Generation with Improved Meta-Learning Meta-Learning for Few-Shot Land Cover Classification Evaluating a Graphical Model-Driven Approach to Codeless Business App Development Learning to Demodulate from Few Pilots via Offline and Online Meta-Learning Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation Alpha MAML: Adaptive Model-Agnostic Meta-Learning HIDRA: Head Initialization across Dynamic targets for Robust Architectures Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Learning to Compare: Relation Network for Few-Shot Learning Meta-Transfer Learning for Few-Shot Learning Meta-Learning for Generalized Zero-Shot Learning HIDRA: Head Initialization across Dynamic targets for Robust Architectures Biased Stochastic Gradient Descent for Conditional Stochastic Optimization Attentive Task-Agnostic Meta-Learning for Few-Shot Text Classification A meta-learning method for histopathology image classification based on LSTM-model Domain-Invariant Speaker Vector Projection by Model-Agnostic Meta-Learning Meta-Relation Networks for Few Shot Learning Coupling Retrieval and Meta-Learning for Context-Dependent Semantic Parsing Provably Convergent Policy Gradient Methods for Model-Agnostic Meta-Reinforcement Learning Topology and simulations of a Hierarchical Markovian Radial Basis Function Neural Network classifier Probabilistic Model-Agnostic Meta-Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Class Regularization: Improve Few-shot Image Classification by Reducing Meta Shift Self-Supervised Learning For Few-Shot Image Classification Transductive Episodic-Wise Adaptive Metric for Few-Shot Learning Learning from Multiple Cities: A Meta-Learning Approach for Spatial-Temporal Prediction Towards Meta-learning of Deep Architectures for Efficient Domain Adaptation Meta-Level Learning in a Hybrid Knowledge-Based Architecture A Systematic Study of Cross-Project Defect Prediction With Meta-Learning Meta-Reinforcement Learning for Trajectory Design in Wireless UAV Networks Meta-Learning for Construction of Resampling Recommendation Systems Smooth Imitation Learning for Online Sequence Prediction Autonomous exploration for navigating in non-stationary CMPs Ontological Modeling of Meta Learning Multi-Agent Systems in OWL-DL MetaFuse: A Pre-trained Fusion Model for Human Pose Estimation Domain-Specific Priors and Meta Learning for Low-shot First-Person Action Recognition Learning to Compare: Relation Network for Few-Shot Learning Meta-Transfer Learning for Few-Shot Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Probabilistic Model-Agnostic Meta-Learning Learning to Generalize from Sparse and Underspecified Rewards
317bded7b76fe789bb61b1b69eb60825e9be6137 1bc5e328f55e4f0e7483f569148a9070821a9245 8237cb7d526b3aad4f28ca0c3be3359b0f5f63fa 34075ea242d6d76e360fb9fe7998ba859151dbca fb180f75b43f008115da66d3cce5f8cbf01b2cbc 552acb0625904e6945f351ffd8aaded10a281ee1 12ea92a54a8c67934aa8740863ef0d3e48cb681d 609d03f5f2fe79f0b949e756a5517349aa761f96 84b888b66d598f4fd5909c524a1818e1004a3254 d569777654c81b66eedc31987e62ad2de5d9564b a936732daa9e7d6c170d7f4b6386e2cf74ea3e0c 801f6058e5a36ba81115ac8751ff6eac1f65f84d af985dea540bd489397e7d28affa10f32a4d7167 165c415e911e913fe9b90f8f19f44c6ef4215a3f 79558382aeabcb56a53cc89fcd06cd8515dbc643 ed315478e4590d2ced5a3e3c849981119f50600e 7d0029510d7f47d3ee716aa8e01b69e353f9e143 4dd26eea6327a053f517f7f840f2d0fe99d6bee8 162d04c2340195b6055f5d28f9e1fdba93075aef a03be7e9055632cbccdf9cee6171089c1e167557 a58e9404695acf3165c4b77798e5bd8bdc6cfcf3 8d4b4b6f0ae7a439072288dfc008ac241541a380 74545aba77cceddff63340fb574b224aee014cda 8d4b4b6f0ae7a439072288dfc008ac241541a380 22c676f5ede0fc250d318a8374d07ec60e80eac1 165c415e911e913fe9b90f8f19f44c6ef4215a3f 49845dcc0836c452968a4799f18b018924385910 45e285fdb1512abf2bdd8f06e381162740d0c630 41ba25b2fffeedbbf10f0b5e7045d70b8b9249eb 1f191dc4923df5fc53d5d375b05c0c32997348b9 ecb0bbee797410b8e8b3272421ffbd8ab4f6da71 cb8c25fb82ac126cc2471e8d60344502342c3341 a2f22d5fa818b5d62ef4dd82cfda30f5be7f80fa 2900a934770afa971564148586e2c31200a7c188 38b547a2cf81bacd30cbb322e7279091753604dc a3553b374f605f261dfb1d58711045010fc06ac9 3be6f55028b23d470a959926c714e34f8cf4d550 fc6c40099bac4787615bbfdef3208b845ec0bf1e 86dab8b9658f8541017b5167b291bad99e5ea64b d5b83880fab1f4c5afa795ce6e2a77874ff4eae0 cf8f80c13239ceace082fc0911172e3fd6157943 ae35a803967981b96d03dc1f90a1bedc43025cdb 9e8f14582200c121a2787a2b5eb5b8ba8c799427 fd1299a7b036da2c1b4a6edbfc656a0bf64b1172 e115961ccba8f212045ba0600ccee493f1192328 18cb8c24fae3186487b14fdb28b1b2617057198f 84b888b66d598f4fd5909c524a1818e1004a3254
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Engineering","Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Engineering","Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Computer Science","Mathematics"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"]
2014 2019 2014 2019 2019 2017 2007 2016 2019 2020 2019 2017 2012 2020 2018 2019 2020 2020 2018 2019 2019 2019 2019 2017 2018 2019 2020 2019 2020 2018 2019 2020 2019 2019 2020 2015 2018 2017 2019 2019 2019 2019 2019 1997 2018 2020 2017 2016 2019 2012 2020 2019 2018 2019 2017 2018 2019
ACM Multimedia AAMAS ICML HICSS NeurIPS ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) AAAI ICACI ACL (1) NeurIPS ICML WWW PRICAI (2) AIME ICML 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) ICML NeurIPS
https://api.semanticscholar.org/317bded7b76fe789bb61b1b69eb60825e9be6137 https://api.semanticscholar.org/1bc5e328f55e4f0e7483f569148a9070821a9245 https://api.semanticscholar.org/8237cb7d526b3aad4f28ca0c3be3359b0f5f63fa https://api.semanticscholar.org/34075ea242d6d76e360fb9fe7998ba859151dbca https://api.semanticscholar.org/fb180f75b43f008115da66d3cce5f8cbf01b2cbc https://api.semanticscholar.org/552acb0625904e6945f351ffd8aaded10a281ee1 https://api.semanticscholar.org/12ea92a54a8c67934aa8740863ef0d3e48cb681d https://api.semanticscholar.org/609d03f5f2fe79f0b949e756a5517349aa761f96 https://api.semanticscholar.org/84b888b66d598f4fd5909c524a1818e1004a3254 https://api.semanticscholar.org/d569777654c81b66eedc31987e62ad2de5d9564b https://api.semanticscholar.org/a936732daa9e7d6c170d7f4b6386e2cf74ea3e0c https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://api.semanticscholar.org/801f6058e5a36ba81115ac8751ff6eac1f65f84d https://api.semanticscholar.org/af985dea540bd489397e7d28affa10f32a4d7167 https://api.semanticscholar.org/165c415e911e913fe9b90f8f19f44c6ef4215a3f https://api.semanticscholar.org/79558382aeabcb56a53cc89fcd06cd8515dbc643 https://api.semanticscholar.org/ed315478e4590d2ced5a3e3c849981119f50600e https://api.semanticscholar.org/7d0029510d7f47d3ee716aa8e01b69e353f9e143 https://api.semanticscholar.org/4dd26eea6327a053f517f7f840f2d0fe99d6bee8 https://api.semanticscholar.org/162d04c2340195b6055f5d28f9e1fdba93075aef https://api.semanticscholar.org/a03be7e9055632cbccdf9cee6171089c1e167557 https://api.semanticscholar.org/a58e9404695acf3165c4b77798e5bd8bdc6cfcf3 https://api.semanticscholar.org/8d4b4b6f0ae7a439072288dfc008ac241541a380 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://www.semanticscholar.org/paper/bfe284e4338e62f0a61bb33398353efd687f206f https://www.semanticscholar.org/paper/888b3c0f4cf66926484d53c934b7eda760a19265 https://api.semanticscholar.org/74545aba77cceddff63340fb574b224aee014cda https://api.semanticscholar.org/8d4b4b6f0ae7a439072288dfc008ac241541a380 https://api.semanticscholar.org/22c676f5ede0fc250d318a8374d07ec60e80eac1 https://api.semanticscholar.org/165c415e911e913fe9b90f8f19f44c6ef4215a3f https://api.semanticscholar.org/49845dcc0836c452968a4799f18b018924385910 https://api.semanticscholar.org/45e285fdb1512abf2bdd8f06e381162740d0c630 https://api.semanticscholar.org/41ba25b2fffeedbbf10f0b5e7045d70b8b9249eb https://api.semanticscholar.org/1f191dc4923df5fc53d5d375b05c0c32997348b9 https://api.semanticscholar.org/ecb0bbee797410b8e8b3272421ffbd8ab4f6da71 https://api.semanticscholar.org/cb8c25fb82ac126cc2471e8d60344502342c3341 https://www.semanticscholar.org/paper/38e2f851b705faa0d0a698ed9885bd6834440073 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://api.semanticscholar.org/a2f22d5fa818b5d62ef4dd82cfda30f5be7f80fa https://api.semanticscholar.org/2900a934770afa971564148586e2c31200a7c188 https://api.semanticscholar.org/38b547a2cf81bacd30cbb322e7279091753604dc https://api.semanticscholar.org/a3553b374f605f261dfb1d58711045010fc06ac9 https://api.semanticscholar.org/3be6f55028b23d470a959926c714e34f8cf4d550 https://api.semanticscholar.org/fc6c40099bac4787615bbfdef3208b845ec0bf1e https://api.semanticscholar.org/86dab8b9658f8541017b5167b291bad99e5ea64b https://api.semanticscholar.org/d5b83880fab1f4c5afa795ce6e2a77874ff4eae0 https://api.semanticscholar.org/cf8f80c13239ceace082fc0911172e3fd6157943 https://api.semanticscholar.org/ae35a803967981b96d03dc1f90a1bedc43025cdb https://api.semanticscholar.org/9e8f14582200c121a2787a2b5eb5b8ba8c799427 https://api.semanticscholar.org/fd1299a7b036da2c1b4a6edbfc656a0bf64b1172 https://api.semanticscholar.org/e115961ccba8f212045ba0600ccee493f1192328 https://api.semanticscholar.org/18cb8c24fae3186487b14fdb28b1b2617057198f https://www.semanticscholar.org/paper/bfe284e4338e62f0a61bb33398353efd687f206f https://www.semanticscholar.org/paper/888b3c0f4cf66926484d53c934b7eda760a19265 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://www.semanticscholar.org/paper/38e2f851b705faa0d0a698ed9885bd6834440073 https://api.semanticscholar.org/84b888b66d598f4fd5909c524a1818e1004a3254