We propose a method of multiclass classification by utilizing a distance mapping learning network that is a distance-based multilayer perceptron The network can obtain the non-linear mapping between the input objects and the outputs by providing a pair of objects and the desired distance between them . It thus realizes multiclass classification based on pairwise classifications iteratively . We show the validity of the model with two classification problems : Iris classification and facial expression classification . Their extension for multiclass problems was the subject of several researches . Usually , a multiclass classifier is obtained by combining several binary classifiers . During the last years , the attention is focused on four main models of Multi-class Support Vector Machines ( M-SVM ) , which consider all classes simultaneously : Classification and prediction problems have a vital role in medical decision making . Disease diagnosis is a multiclass classification problem . The classification in disease diagnosis is t o assign a disease label to a particular instance . In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation ( VVRKFA ) . VVRKFA being an extension of fast regularized kernel function approximation ( FRKFA ) , provides the vector-valued response at single step . Based on the analysis of the basic theory of in the new method for processing the no-line date — The Kernel Methods , the basic principles of the multiclass classifying based on the One-class Classification have been studied . The function of for multiclass classifying has been put forward . For most datasets , classification accuracy of the same classifiers can rise high without excluding many instances . The empirical study shows that this idea works well especially for the multiclass classification case . Then , in order to reduce the complexity and benefit the classification , the high dimensional features are projected to a low dimensional representation with manifold learning . Finally , a multiclass classification process is performed on this new low dimensional representation of the image and the quality assessment is based on the learned classification result in order to respect the one of the human observers . Instead of computing a final note , our method classifies the quality according to the quality scale recommended by the ITU . When training and testing the 33 cohorts of cancer types in the convolutional neural network , our classification model led to an accuracy of 95.65 % . This result is reasonably good when compared with existing works that use multiclass label classification . We also examine the genes based on their significance related to cancer types through the heat map and associate them with biomarkers . ( MIAMI HEAT , 95 , POINTS ) . Indeed , many relation extraction systems reduce relation extraction to multi - class classification precisely in this way [ reference ][ reference ][ reference ][ reference ] . More concretely , given a documentŷ 1:T , we consider all pairs of word - spans in each sentence that represent possible entities e and values This lead narrows as the training data increases , but DeepWalk ends with a 3 % lead in Micro - F1 , and an impressive 5 % improvement in Macro - F1 . This experiment showcases the performance benefits that can occur from using social representation learning for multilabel classification . DeepWalk , can scale to large graphs , and performs exceedingly well in such a sparsely labeled environment . We associate each keypoint an individual label and assign all the non - keypoints a background class label . The keypoint prediction problem could be treated as a multi - class classification problem and the cross - entropy loss is optimized during training . We evaluate our approach against previous state - of - the - art method . For classification this usually means matching the probabilities either via on the scale or by cross - entropy . Concretely , assume we are learning a multi - class classifier over a data set of examples of the form with possible classes . The usual training criteria is to minimize NLL for each example from the training data , where is the indicator function and the distribution from our model ( parameterized by ) . The model includes an image analysis part and a caption generation part . In the image analysis part , we first use supervised learning to predict a set of attributes , based on words commonly found in image captions We solve this as a multi - label classification problem and train a corresponding deep CNN by minimizing an element - wise logistic loss function . Secondly , a fixed length vector is created for each image , whose length is the size of the attribute set . We get the probability of all labels simply by taking the product of each conditional . Our approach thus performs a multiclass labeling by assigning to each pixel the label with the largest probability . subsection : The Architectures Detailed region annotations upon stereotaxic coordinates are highly rare , prompting the need of using one or very few available annotated results of a specific brain section to label images of broadly accessible brain section samples . Here we develop a one-shot learning approach to segment regions of mouse brains . Using the highly ordered geometry of brains , we introduce a reference mask to incorporate both the anatomical structure ( visual information ) and the brain atlas into brain segmentation . The hand trajectories were classified into different classes ( commands ) with a recognition accuracy of 95.9 % . In addition , the new approach was validated with the “ one shot learning ” paradigm with comparable results to those reported in 2012 . In a validation experiment , the gestures were used to control a mobile service robot and a robotic arm in a laboratory chemistry experiment . The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data . Then , in real-time , DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network , to maximize the objective function . Second , we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields . Gestures are both natural and intuitive for Human-Computer-Interaction ( HCI ) and the one-shot learning scenario is one of the real world situations in terms of gesture recognition problems . In this demo , we present a hand gesture recognition system using the Kinect sensor , which addresses the problem of one-shot learning gesture recognition with a user-defined training and testing system . Furthermore , this paper presents an end-to-end learning framework for training adaptive kernel SVMs , which eliminates the problem of choosing a correct kernel and good features for SVMs . Next , the one-shot learning problem is redefined for audio signals . Then the model was tested on vision task ( using Omniglot dataset ) and speech task ( using TIMIT dataset ) as well . These two components appear to balance reward for matching a specific instance of behaviour versus that behaviour in general . Furthermore , we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task -- the one-shot learning setting . We demonstrate our approach on humanoid agents in both 2D with $ 10 $ degrees of freedom ( DoF ) and 3D with $ 38 $ DoF. Previous work to learn tasks through observing a demonstration ( e.g. , Lent & Laird , 2001 ) has required observing many examples of the same task . One-shot learning of tasks presents a significant challenge because the observed sequence is inherently incomplete -- the user only performs the steps required for the current situation . Furthermore , their decision-making processes , which reflect the control structures in the procedure , are not revealed . We model a " one-shot learning " situation , where very few observations y1, ... ,yn ∈ ℝd are available . Associated with each observation yi is a very high-dimensional vector xi ∈ ℝd , which provides context for yi and enables us to predict subsequent observations , given their own context . In this work , a one-shot learning solution to the t-maze road sign problem is presented . This problem consists in taking the correct turning decision at a bifurcation after seeing a light signal some time steps before . As a result , recent more scalable approaches to few - shot learning have focused on acquiring deterministic learning algorithms that disregard ambiguity over the underlying function . Can we develop an approach that has the benefits of both classes of few - shot learning methods - scalability and uncertainty awareness ? To do so , we build upon tools in amortized variational inference for developing a probabilistic meta - learning approach . Our experiments show that our approach improves on MAML [ reference ] , which outperforms the model by Edwards and Storkey [ reference ] . Other work that considers model uncertainty in the few - shot learning setting is the LLAMA method [ reference ] , which also builds on the MAML algorithm . LLAMA makes use of a local Laplace approximation for modeling the task parameters ( post - update parameters ) , which introduces the need to approximate a high dimensional covariance matrix . * 45.10 ± 0.00 % prototypical networks [ reference ] 46.61 ± 0.78 % mAP - DLM [ reference ] 49.82 ± 0.78 % GNN [ reference ] 50.33 ± 0.36 % Relation Net [ reference ] 50.44 ± 0.82 % Table 2 : Comparison between our approach and prior MAML - based methods ( top ) , and other prior few - shot learning techniques on the 5 - way , 1 - shot MiniImagenet benchmark . Our approach gives a small boost over MAML , and is comparable to other approaches . document : Matching Networks for One Shot Learning Learning from a few examples remains a key challenge in machine learning . ‘ ‘ one - shot ’ ’ learning , which consists of learning a class from a single labelled example . Deep learning has made major advances in areas such as speech , vision and language , but is notorious for requiring large datasets . Lake et al . [ reference ] have been able to learn representations using probabilistic inference over Bayesian programs , which achieved convincing one - shot learning results on the OMNI dataset . In addition , prior research attempted to learn disentangled representations using supervised data . Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications . In this paper , we present Few-shot Classifier Generative Adversarial Network as an approach for few-shot classification . We address the problem of few-shot classification by designing a GAN model in which the discriminator and the generator compete to output labeled data in any case . In effect , our method trains the model to be easy to fine - tune . We demonstrate that this approach leads to state - of - the - art performance on two fewshot image classification benchmarks , produces good results on few - shot regression , and accelerates fine - tuning for policy gradient reinforcement learning with neural network policies . section : Introduction [ reference ] . Unlike these methods , the MAML learner 's weights are updated using the gradient , rather than a learned update ; our method does not introduce additional parameters for meta - learning nor require a particular learner architecture . Few - shot learning methods have also been developed for specific tasks such as generative modeling [ reference ][ reference ] and image recognition [ reference ] . One successful approach for few - shot classification is to learn to compare new examples in a learned metric space using e.g. Siamese networks [ reference ] or recurrence with attention mechanisms [ reference ][ reference ][ reference ] . These approaches have generated some of the most successful results , but are difficult to directly extend to other problems , such as reinforcement learning . Our method , in contrast , is agnostic to the form of the model and to the particular learning task . ResultsVarious kinds of molecular descriptors were calculated to represent the molecular structures of the compounds . The original data set was partitioned into the training and test sets by the K-means classification method . Prediction error on the test data set indicated that the XCSF as a local model estimates caspase-3 inhibition activity , better than the global models such as MLR and ANN . Research highlights We propose a semi-supervised learning method by using the multi-class boosting . It handles K-class classification without reducing into multiple two-class problems . The classifi ... In this talk , I will present Merged-Averaged Classifiers via Hashing ( MACH ) for K-classification with ultra-large values of K. Compared to traditional one-vs-all classifiers that require $ O(Kd)$ memory and inference cost , MACH only need $ O(dłogK)$ ( d is dimensionality ) memory while only requiring $ O(KłogK + dłogK ) $ operation for inference . MACH is a generic K-classification algorithm , with provably theoretical guarantees , without any assumption on the relationship between classes . MACH uses universal hashing to reduce classification with a large number of classes to few ( logarithmic many ) independent classification tasks with small ( constant ) number of classes . K-Nearest Neighbor and Support Vector Machine are the two classifiers used for identifying the character in the problem . In k-nearest classification method , the Euclidean distance between the test point and all the reference points is calculated in order to find K nearest neighbors , and then the obtained distances are ranked in ascending order and the reference points corresponding to the k smallest Euclidean distances are taken . The Support Vector Machine ( SVM ) is learning machine with very good generalization ability . The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation . We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem . Such an approach is viable only with large training sets such as ours . We propose a simple approach to visual alignment , focusing on the illustrative task of facial landmark estimation . While most prior work treats this as a regression problem , we instead formulate it as a discrete $ K$-way classification task , where a classifier is trained to return one of $ K$ discrete alignments . One crucial benefit of a classifier is the ability to report back a ( softmax ) distribution over putative alignments . To assign the class for a new entry , machines ’ outputs are evaluated in a specific pulling scheme . This article introduces the “ Support Vector Classification-Regression ” machine for K -class classification purposes ( K -SVCR ) , a new training algorithm with ternary outputs { −1,0,+1 } based on Vapnik 's Support Vector theory . This new machine evaluates all the training data into a 1-versus-1-versus-rest structure during the decomposing phase by using a mixed classification and regression SV Machine ( SVM ) formulation . This paper proposes to characterize the GNSS signals reception environment using image analysis . The proposed approach consists of detecting the visible sky part in the images thanks to a k-means based classification algorithm applied in segmented/simplified images . In this paper , two images segmentation/simplification techniques are proposed and compared in terms of robustness , reliability and processing rate . In this case , aspect is dealt with by explicit 3D reasoning . An alternative approach is to model the whole problem as k-way classification into a set of known classes . This approach is much more accurate at present , but has the difficulty that we do n't really know what the classes should be in general . Each accepts a set of inputs and a set of parameters , and produces a number which is a non-linear function of the inputs and the parameters . It is straightforward to produce a k way classifier out of k units . Our motivating example is object instance segmentation , where the model generates a collection of segments corresponding to object instances . This problem ca n’t be treated as k - way classification since the number of objects is n’t known in advance . Further , the loss should be invariant to permutations of the instance labels within the same semantic category . As a final test of our method , we also train it to produce semantic labels which are combined with our instance proposal method to recognize the detected proposals . For semantic segmentation which is a k - way classification problem , we train a model using cross - entropy loss alongside our embedding loss . Similar to our proposal detection model , we use a 64 - dimension embedding space on top of DeepLab - v3 as our base model . [ π ; 3π / 2 [ , and r 4 = [ 3π / 2 ; 2π [ , and we train the classifier k ( · ) to predict in which of these four regions the angle of rotation β is . If β ∈ r 2 or β ∈ r 4 , we mirror the image before computing the pose as before . The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances . Once this embedding has been produced , then the aforementioned tasks become straight - forward : face verification simply involves thresholding the distance between the two embeddings ; recognition becomes a k - NN classification problem ; and clustering can be achieved using off - the - shelf techniques such as k - means or agglomerative clustering . Previous face recognition approaches based on deep networks use a classification layer trained over a set of known face identities and then take an intermediate bottleneck layer as a representation used to generalize recognition beyond the set of identities used in training . First , by using a multiple model estimator , a more expressive hybrid dynamical model is imposed on the target . Then , the estimator is augmented to input the discrete classification from the secondary vision algorithm by recasting the process and sensor models as a dynamic Bayesian network ( DBN ) . By leveraging the information in the body motion classifications , the estimator is able to detect mode changes before the resulting changes in velocity are apparent and a significant improvement in velocity estimation is realized . The partitioning of space by hyperplanes in the context of discrete classification problem is considered . We obtain some relations for the number of partitions and establish a recurrence relation for the maximal number of partitions of R^n by m hyperplanes . In incipient fault diagnosis tasks , the proposed approach outperformed some of the conventional techniques . Moreover , the performance of the proposed approach is better than typical discrete based classification techniques employing some monitoring indexes such as the false alarm rate , detection time and diagnosis time . This model is capable of reproducing a rich variety of neuronal spike responses , including regular spiking , intrinsic bursting , fast spiking , and chattering , by adjusting only three adaptive threshold parameters . This model can express a continuous variety of the firing characteristics in a three-dimensional parameter space rather than just those identified in the conventional discrete categorization . Both high flexibility and low computational cost would help to model the real brain function faithfully and examine how network properties may be influenced by the distributed characteristics of component neurons . Continuous emotion dimension prediction has increased in popularity over the last few years , as the shift away from discrete classification based tasks has introduced more realism in emotion modeling . However , many questions remain including how best to combine information from several modalities ( e.g. audio , video , etc ) . Discrete classification problems are important in pattern recognition applications . The most often used discrete classification rule is the discrete histogram rule . In this letter we provide exact expressions for the correlation coefficient between the actual error and the resubstitution and leave-one-out cross-validation error estimators for the discrete histogram rule . Next to a global discrete land cover map at 100 m resolution , a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel . This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme . Overall , 20 layers are provided which allow customization of land cover maps to specific user needs or applications ( e.g. , forest monitoring , crop monitoring , biodiversity and conservation , climate modeling , etc . ) . In K - shot regression tasks , K input / output pairs are provided for learning for each task . Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning More generally , learning from paired data has also been explored in co - clustering and in other works that build on the information bottleneck principle . Several recent papers have used information as a tool to train deep networks in particular , albeit not for discrete clustering . IMSAT maximises mutual information between data and its representation and DeepINFOMAX maximizes information between spatially - preserved features and compact features . Meta-learning approaches have been proposed to tackle the few-shot learning problem . Typically , a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks . Given a latent representation , the proposed framework draws a set of interpretable factors , each aligned to independent factors of variations by maximizing their total correlation in an information-theoretic means . As a plug-in method , we have applied our proposed FDEN to the existing networks of Adversarially Learned Inference and Pioneer Network and conducted computer vision tasks of image-to-image translation in semantic ways , e.g. , changing styles while keeping an identify of a subject , and object classification in a few-shot learning scheme . We have also validated the effectiveness of our method with various ablation studies in qualitative , quantitative , and statistical examination . As a result , deep learning technology still can not yield satisfactory result in ear recognition area . In this paper , we tackle ear recognition problem by using few-shot learning based methods . We explore different methods towards model training with limited amounts of training data and show that by using them , with the help of data augmentation , the model can be flexible and can quickly adapt to new identity to perform fast recognition . [ reference ] . However , when the end goal of few - shot meta - learning is to learn solutions to new tasks from small amounts of data , a critical issue that must be dealt with is task ambiguity : even with the best possible prior , there might simply not be enough information in the examples for a new task to resolve that task with high certainty . It is therefore quite desireable to develop few - shot meta - learning methods that can propose multiple potential solutions to an ambiguous few - shot learning problem . Such a method could be used to evaluate uncertainty ( by measuring agreement between the samples ) , perform active learning , or elicit direct human supervision about which sample is preferable . Finally , while we do not experiment with this in this paper , we expect that modeling this ambiguity will be helpful for reinforcement learning problems , where it can be used to aid in exploration . While recognizing and accounting for ambiguity is an important aspect of the few - shot learning problem , it is particularly challenging to model when scaling to high - dimensional data , large function approximators , and multimodal task structure . Representing distributions over functions is relatively straightforward when using simple function approximators , such as linear functions , and has been done extensively in early few - shot learning approaches using Bayesian models Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning Require : p ( T ) : distribution over tasks Require : α , β : step size hyperparameters 1 : randomly initialize θ 2 : while not done do 3 : In our first proposal , portfolio adaptation , a set of induction models is pretrained on a set of related tasks , and the best model is adapted towards the new task using transfer learning . In our second approach , meta program induction , a $ k$-shot learning approach is used to make a model generalize to new tasks without additional training . To test the efficacy of our methods , we constructed a new benchmark of programs written in the Karel programming language . Training a classifier with only a few examples remains a significant barrier when using neural networks with large number of parameters . Though various specialized network architectures have been proposed for these k-shot learning tasks to avoid overfitting , a question remains : is there a generalizable framework for the k-shot learning problem that can leverage existing deep models as well as avoid model overfitting ? In this paper , we proposed a generalizable k-shot learning framework that can be used on any pre-trained network , by grouping network parameters to produce a low-dimensional representation of the parameter space . In our meta - learning scenario , we consider a distribution over tasks p ( T ) that we want our model to be able to adapt to . In the K - shot learning setting , the model is trained to learn a new task T i drawn from p ( T ) from only K samples drawn from q i and feedback L Ti generated by T i . The loss for task T i and model f φ takes the form In K - shot reinforcement learning , K rollouts from f θ and task T i , ( x 1 , a 1 , ... [ reference ][ reference ] . In contrast , our method explicitly trains the parameters for sensitivity on a given task distribution , allowing for extremely efficient adaptation for problems such as K - shot learning and rapid reinforcement learning in only one or a few gradient steps . section : Experimental Evaluation An improved super-resolution algorithm through neighbor embedding with new feature selection and example training is proposed for single image super resolution reconstruction . Firstly , we take the DCT coefficients as the feature vectors , and then adaptively choose neighbors by k-means clustering algorithm . The financial market is very fickle and investors have the difficult task of following and trying to predict the swings of the market so that their strategies result in better financial returns . With the use of Big Data and Bayesian mathematical statistics based on prior knowledge and examples of training to determine the likelihood of a hypothesis , financial news can be tracked continuously and affecting key financial indicators , Actions and assisting the investor . The objective of this work is to propose a stock price prediction process of a company listed on the Ibovespa to support decisions in the financial market , helping an investor to better buy and sell stocks using a predictive system with Big Data and Natural Processing Language ( LPN ) to collect real-time information from news sites that may influence stock prices . Next we describe our training solution that takes advantage of these GPU implementations while still preserving the spatial pyramid pooling behaviors . subsubsection : Single - size training As in previous works , we first consider a network taking a fixed - size input ( 224 224 ) cropped from images . This result is achieved without increasing the training time ( due to the same number of mini - batches ) and without fine - tuning . We argue that this is a promising result , given that the training task of classifying 5 K categories is a more challenging one . section : Experiments on CIFAR
We propose a method of multiclass classification by utilizing a distance mapping learning network that is a distance-based multilayer perceptron The network can obtain the non-linear mapping between the input objects and the outputs by providing a pair of objects and the desired distance between them . It thus realizes multiclass classification based on pairwise classifications iteratively . We show the validity of the model with two classification problems : Iris classification and facial expression classification .
Their extension for multiclass problems was the subject of several researches . Usually , a multiclass classifier is obtained by combining several binary classifiers . During the last years , the attention is focused on four main models of Multi-class Support Vector Machines ( M-SVM ) , which consider all classes simultaneously :
Classification and prediction problems have a vital role in medical decision making . Disease diagnosis is a multiclass classification problem . The classification in disease diagnosis is t o assign a disease label to a particular instance .
In this brief we have proposed the multiclass data classification by computationally inexpensive discriminant analysis through vector-valued regularized kernel function approximation ( VVRKFA ) . VVRKFA being an extension of fast regularized kernel function approximation ( FRKFA ) , provides the vector-valued response at single step .
Based on the analysis of the basic theory of in the new method for processing the no-line date — The Kernel Methods , the basic principles of the multiclass classifying based on the One-class Classification have been studied . The function of for multiclass classifying has been put forward .
For most datasets , classification accuracy of the same classifiers can rise high without excluding many instances . The empirical study shows that this idea works well especially for the multiclass classification case .
Then , in order to reduce the complexity and benefit the classification , the high dimensional features are projected to a low dimensional representation with manifold learning . Finally , a multiclass classification process is performed on this new low dimensional representation of the image and the quality assessment is based on the learned classification result in order to respect the one of the human observers . Instead of computing a final note , our method classifies the quality according to the quality scale recommended by the ITU .
When training and testing the 33 cohorts of cancer types in the convolutional neural network , our classification model led to an accuracy of 95.65 % . This result is reasonably good when compared with existing works that use multiclass label classification . We also examine the genes based on their significance related to cancer types through the heat map and associate them with biomarkers .
( MIAMI HEAT , 95 , POINTS ) . Indeed , many relation extraction systems reduce relation extraction to multi - class classification precisely in this way [ reference ][ reference ][ reference ][ reference ] . More concretely , given a documentŷ 1:T , we consider all pairs of word - spans in each sentence that represent possible entities e and values
This lead narrows as the training data increases , but DeepWalk ends with a 3 % lead in Micro - F1 , and an impressive 5 % improvement in Macro - F1 . This experiment showcases the performance benefits that can occur from using social representation learning for multilabel classification . DeepWalk , can scale to large graphs , and performs exceedingly well in such a sparsely labeled environment .
We associate each keypoint an individual label and assign all the non - keypoints a background class label . The keypoint prediction problem could be treated as a multi - class classification problem and the cross - entropy loss is optimized during training . We evaluate our approach against previous state - of - the - art method .
For classification this usually means matching the probabilities either via on the scale or by cross - entropy . Concretely , assume we are learning a multi - class classifier over a data set of examples of the form with possible classes . The usual training criteria is to minimize NLL for each example from the training data , where is the indicator function and the distribution from our model ( parameterized by ) .
The model includes an image analysis part and a caption generation part . In the image analysis part , we first use supervised learning to predict a set of attributes , based on words commonly found in image captions We solve this as a multi - label classification problem and train a corresponding deep CNN by minimizing an element - wise logistic loss function . Secondly , a fixed length vector is created for each image , whose length is the size of the attribute set .
We get the probability of all labels simply by taking the product of each conditional . Our approach thus performs a multiclass labeling by assigning to each pixel the label with the largest probability . subsection : The Architectures
Detailed region annotations upon stereotaxic coordinates are highly rare , prompting the need of using one or very few available annotated results of a specific brain section to label images of broadly accessible brain section samples . Here we develop a one-shot learning approach to segment regions of mouse brains . Using the highly ordered geometry of brains , we introduce a reference mask to incorporate both the anatomical structure ( visual information ) and the brain atlas into brain segmentation .
The hand trajectories were classified into different classes ( commands ) with a recognition accuracy of 95.9 % . In addition , the new approach was validated with the “ one shot learning ” paradigm with comparable results to those reported in 2012 . In a validation experiment , the gestures were used to control a mobile service robot and a robotic arm in a laboratory chemistry experiment .
The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data . Then , in real-time , DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network , to maximize the objective function . Second , we develop the first open-source software for practical artificially intelligent one-shot classification systems with limited resources for the benefit of researchers in related fields .
Gestures are both natural and intuitive for Human-Computer-Interaction ( HCI ) and the one-shot learning scenario is one of the real world situations in terms of gesture recognition problems . In this demo , we present a hand gesture recognition system using the Kinect sensor , which addresses the problem of one-shot learning gesture recognition with a user-defined training and testing system .
Furthermore , this paper presents an end-to-end learning framework for training adaptive kernel SVMs , which eliminates the problem of choosing a correct kernel and good features for SVMs . Next , the one-shot learning problem is redefined for audio signals . Then the model was tested on vision task ( using Omniglot dataset ) and speech task ( using TIMIT dataset ) as well .
These two components appear to balance reward for matching a specific instance of behaviour versus that behaviour in general . Furthermore , we focus here on a particularly challenging form of this problem where only a single demonstration is provided for a given task -- the one-shot learning setting . We demonstrate our approach on humanoid agents in both 2D with $ 10 $ degrees of freedom ( DoF ) and 3D with $ 38 $ DoF.
Previous work to learn tasks through observing a demonstration ( e.g. , Lent & Laird , 2001 ) has required observing many examples of the same task . One-shot learning of tasks presents a significant challenge because the observed sequence is inherently incomplete -- the user only performs the steps required for the current situation . Furthermore , their decision-making processes , which reflect the control structures in the procedure , are not revealed .
We model a " one-shot learning " situation , where very few observations y1, ... ,yn ∈ ℝd are available . Associated with each observation yi is a very high-dimensional vector xi ∈ ℝd , which provides context for yi and enables us to predict subsequent observations , given their own context .
In this work , a one-shot learning solution to the t-maze road sign problem is presented . This problem consists in taking the correct turning decision at a bifurcation after seeing a light signal some time steps before .
As a result , recent more scalable approaches to few - shot learning have focused on acquiring deterministic learning algorithms that disregard ambiguity over the underlying function . Can we develop an approach that has the benefits of both classes of few - shot learning methods - scalability and uncertainty awareness ? To do so , we build upon tools in amortized variational inference for developing a probabilistic meta - learning approach .
Our experiments show that our approach improves on MAML [ reference ] , which outperforms the model by Edwards and Storkey [ reference ] . Other work that considers model uncertainty in the few - shot learning setting is the LLAMA method [ reference ] , which also builds on the MAML algorithm . LLAMA makes use of a local Laplace approximation for modeling the task parameters ( post - update parameters ) , which introduces the need to approximate a high dimensional covariance matrix .
* 45.10 ± 0.00 % prototypical networks [ reference ] 46.61 ± 0.78 % mAP - DLM [ reference ] 49.82 ± 0.78 % GNN [ reference ] 50.33 ± 0.36 % Relation Net [ reference ] 50.44 ± 0.82 % Table 2 : Comparison between our approach and prior MAML - based methods ( top ) , and other prior few - shot learning techniques on the 5 - way , 1 - shot MiniImagenet benchmark . Our approach gives a small boost over MAML , and is comparable to other approaches .
document : Matching Networks for One Shot Learning Learning from a few examples remains a key challenge in machine learning .
‘ ‘ one - shot ’ ’ learning , which consists of learning a class from a single labelled example . Deep learning has made major advances in areas such as speech , vision and language , but is notorious for requiring large datasets .
Lake et al . [ reference ] have been able to learn representations using probabilistic inference over Bayesian programs , which achieved convincing one - shot learning results on the OMNI dataset . In addition , prior research attempted to learn disentangled representations using supervised data .
Fine-grained image classification with a few-shot classifier is a highly challenging open problem at the core of a numerous data labeling applications . In this paper , we present Few-shot Classifier Generative Adversarial Network as an approach for few-shot classification . We address the problem of few-shot classification by designing a GAN model in which the discriminator and the generator compete to output labeled data in any case .
In effect , our method trains the model to be easy to fine - tune . We demonstrate that this approach leads to state - of - the - art performance on two fewshot image classification benchmarks , produces good results on few - shot regression , and accelerates fine - tuning for policy gradient reinforcement learning with neural network policies . section : Introduction
[ reference ] . Unlike these methods , the MAML learner 's weights are updated using the gradient , rather than a learned update ; our method does not introduce additional parameters for meta - learning nor require a particular learner architecture . Few - shot learning methods have also been developed for specific tasks such as generative modeling [ reference ][ reference ] and image recognition [ reference ] . One successful approach for few - shot classification is to learn to compare new examples in a learned metric space using e.g. Siamese networks [ reference ] or recurrence with attention mechanisms [ reference ][ reference ][ reference ] . These approaches have generated some of the most successful results , but are difficult to directly extend to other problems , such as reinforcement learning . Our method , in contrast , is agnostic to the form of the model and to the particular learning task .
ResultsVarious kinds of molecular descriptors were calculated to represent the molecular structures of the compounds . The original data set was partitioned into the training and test sets by the K-means classification method . Prediction error on the test data set indicated that the XCSF as a local model estimates caspase-3 inhibition activity , better than the global models such as MLR and ANN .
Research highlights We propose a semi-supervised learning method by using the multi-class boosting . It handles K-class classification without reducing into multiple two-class problems . The classifi ...
In this talk , I will present Merged-Averaged Classifiers via Hashing ( MACH ) for K-classification with ultra-large values of K. Compared to traditional one-vs-all classifiers that require $ O(Kd)$ memory and inference cost , MACH only need $ O(dłogK)$ ( d is dimensionality ) memory while only requiring $ O(KłogK + dłogK ) $ operation for inference . MACH is a generic K-classification algorithm , with provably theoretical guarantees , without any assumption on the relationship between classes . MACH uses universal hashing to reduce classification with a large number of classes to few ( logarithmic many ) independent classification tasks with small ( constant ) number of classes .
K-Nearest Neighbor and Support Vector Machine are the two classifiers used for identifying the character in the problem . In k-nearest classification method , the Euclidean distance between the test point and all the reference points is calculated in order to find K nearest neighbors , and then the obtained distances are ranked in ascending order and the reference points corresponding to the k smallest Euclidean distances are taken . The Support Vector Machine ( SVM ) is learning machine with very good generalization ability .
The resulting images are used to train an end-to-end CNN for full-body 3D pose estimation . We cluster the training data into a large number of pose classes and tackle pose estimation as a K-way classification problem . Such an approach is viable only with large training sets such as ours .
We propose a simple approach to visual alignment , focusing on the illustrative task of facial landmark estimation . While most prior work treats this as a regression problem , we instead formulate it as a discrete $ K$-way classification task , where a classifier is trained to return one of $ K$ discrete alignments . One crucial benefit of a classifier is the ability to report back a ( softmax ) distribution over putative alignments .
To assign the class for a new entry , machines ’ outputs are evaluated in a specific pulling scheme . This article introduces the “ Support Vector Classification-Regression ” machine for K -class classification purposes ( K -SVCR ) , a new training algorithm with ternary outputs { −1,0,+1 } based on Vapnik 's Support Vector theory . This new machine evaluates all the training data into a 1-versus-1-versus-rest structure during the decomposing phase by using a mixed classification and regression SV Machine ( SVM ) formulation .
This paper proposes to characterize the GNSS signals reception environment using image analysis . The proposed approach consists of detecting the visible sky part in the images thanks to a k-means based classification algorithm applied in segmented/simplified images . In this paper , two images segmentation/simplification techniques are proposed and compared in terms of robustness , reliability and processing rate .
In this case , aspect is dealt with by explicit 3D reasoning . An alternative approach is to model the whole problem as k-way classification into a set of known classes . This approach is much more accurate at present , but has the difficulty that we do n't really know what the classes should be in general .
Each accepts a set of inputs and a set of parameters , and produces a number which is a non-linear function of the inputs and the parameters . It is straightforward to produce a k way classifier out of k units .
Our motivating example is object instance segmentation , where the model generates a collection of segments corresponding to object instances . This problem ca n’t be treated as k - way classification since the number of objects is n’t known in advance . Further , the loss should be invariant to permutations of the instance labels within the same semantic category .
As a final test of our method , we also train it to produce semantic labels which are combined with our instance proposal method to recognize the detected proposals . For semantic segmentation which is a k - way classification problem , we train a model using cross - entropy loss alongside our embedding loss . Similar to our proposal detection model , we use a 64 - dimension embedding space on top of DeepLab - v3 as our base model .
[ π ; 3π / 2 [ , and r 4 = [ 3π / 2 ; 2π [ , and we train the classifier k ( · ) to predict in which of these four regions the angle of rotation β is . If β ∈ r 2 or β ∈ r 4 , we mirror the image before computing the pose as before .
The network is trained such that the squared L2 distances in the embedding space directly correspond to face similarity : faces of the same person have small distances and faces of distinct people have large distances . Once this embedding has been produced , then the aforementioned tasks become straight - forward : face verification simply involves thresholding the distance between the two embeddings ; recognition becomes a k - NN classification problem ; and clustering can be achieved using off - the - shelf techniques such as k - means or agglomerative clustering . Previous face recognition approaches based on deep networks use a classification layer trained over a set of known face identities and then take an intermediate bottleneck layer as a representation used to generalize recognition beyond the set of identities used in training .
First , by using a multiple model estimator , a more expressive hybrid dynamical model is imposed on the target . Then , the estimator is augmented to input the discrete classification from the secondary vision algorithm by recasting the process and sensor models as a dynamic Bayesian network ( DBN ) . By leveraging the information in the body motion classifications , the estimator is able to detect mode changes before the resulting changes in velocity are apparent and a significant improvement in velocity estimation is realized .
The partitioning of space by hyperplanes in the context of discrete classification problem is considered . We obtain some relations for the number of partitions and establish a recurrence relation for the maximal number of partitions of R^n by m hyperplanes .
In incipient fault diagnosis tasks , the proposed approach outperformed some of the conventional techniques . Moreover , the performance of the proposed approach is better than typical discrete based classification techniques employing some monitoring indexes such as the false alarm rate , detection time and diagnosis time .
This model is capable of reproducing a rich variety of neuronal spike responses , including regular spiking , intrinsic bursting , fast spiking , and chattering , by adjusting only three adaptive threshold parameters . This model can express a continuous variety of the firing characteristics in a three-dimensional parameter space rather than just those identified in the conventional discrete categorization . Both high flexibility and low computational cost would help to model the real brain function faithfully and examine how network properties may be influenced by the distributed characteristics of component neurons .
Continuous emotion dimension prediction has increased in popularity over the last few years , as the shift away from discrete classification based tasks has introduced more realism in emotion modeling . However , many questions remain including how best to combine information from several modalities ( e.g. audio , video , etc ) .
Discrete classification problems are important in pattern recognition applications . The most often used discrete classification rule is the discrete histogram rule . In this letter we provide exact expressions for the correlation coefficient between the actual error and the resubstitution and leave-one-out cross-validation error estimators for the discrete histogram rule .
Next to a global discrete land cover map at 100 m resolution , a set of cover fraction layers is provided depicting the percentual cover of the main land cover types in a pixel . This additional continuous classification scheme represents areas of heterogeneous land cover better than the standard discrete classification scheme . Overall , 20 layers are provided which allow customization of land cover maps to specific user needs or applications ( e.g. , forest monitoring , crop monitoring , biodiversity and conservation , climate modeling , etc . ) .
In K - shot regression tasks , K input / output pairs are provided for learning for each task . Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning
More generally , learning from paired data has also been explored in co - clustering and in other works that build on the information bottleneck principle . Several recent papers have used information as a tool to train deep networks in particular , albeit not for discrete clustering . IMSAT maximises mutual information between data and its representation and DeepINFOMAX maximizes information between spatially - preserved features and compact features .
Meta-learning approaches have been proposed to tackle the few-shot learning problem . Typically , a meta-learner is trained on a variety of tasks in the hopes of being generalizable to new tasks .
Given a latent representation , the proposed framework draws a set of interpretable factors , each aligned to independent factors of variations by maximizing their total correlation in an information-theoretic means . As a plug-in method , we have applied our proposed FDEN to the existing networks of Adversarially Learned Inference and Pioneer Network and conducted computer vision tasks of image-to-image translation in semantic ways , e.g. , changing styles while keeping an identify of a subject , and object classification in a few-shot learning scheme . We have also validated the effectiveness of our method with various ablation studies in qualitative , quantitative , and statistical examination .
As a result , deep learning technology still can not yield satisfactory result in ear recognition area . In this paper , we tackle ear recognition problem by using few-shot learning based methods . We explore different methods towards model training with limited amounts of training data and show that by using them , with the help of data augmentation , the model can be flexible and can quickly adapt to new identity to perform fast recognition .
[ reference ] . However , when the end goal of few - shot meta - learning is to learn solutions to new tasks from small amounts of data , a critical issue that must be dealt with is task ambiguity : even with the best possible prior , there might simply not be enough information in the examples for a new task to resolve that task with high certainty . It is therefore quite desireable to develop few - shot meta - learning methods that can propose multiple potential solutions to an ambiguous few - shot learning problem . Such a method could be used to evaluate uncertainty ( by measuring agreement between the samples ) , perform active learning , or elicit direct human supervision about which sample is preferable .
Finally , while we do not experiment with this in this paper , we expect that modeling this ambiguity will be helpful for reinforcement learning problems , where it can be used to aid in exploration . While recognizing and accounting for ambiguity is an important aspect of the few - shot learning problem , it is particularly challenging to model when scaling to high - dimensional data , large function approximators , and multimodal task structure . Representing distributions over functions is relatively straightforward when using simple function approximators , such as linear functions , and has been done extensively in early few - shot learning approaches using Bayesian models
Similarly , for discrete classification tasks with a crossentropy loss , the loss takes the form : section : Algorithm 2 MAML for Few - Shot Supervised Learning Require : p ( T ) : distribution over tasks Require : α , β : step size hyperparameters 1 : randomly initialize θ 2 : while not done do 3 :
In our first proposal , portfolio adaptation , a set of induction models is pretrained on a set of related tasks , and the best model is adapted towards the new task using transfer learning . In our second approach , meta program induction , a $ k$-shot learning approach is used to make a model generalize to new tasks without additional training . To test the efficacy of our methods , we constructed a new benchmark of programs written in the Karel programming language .
Training a classifier with only a few examples remains a significant barrier when using neural networks with large number of parameters . Though various specialized network architectures have been proposed for these k-shot learning tasks to avoid overfitting , a question remains : is there a generalizable framework for the k-shot learning problem that can leverage existing deep models as well as avoid model overfitting ? In this paper , we proposed a generalizable k-shot learning framework that can be used on any pre-trained network , by grouping network parameters to produce a low-dimensional representation of the parameter space .
In our meta - learning scenario , we consider a distribution over tasks p ( T ) that we want our model to be able to adapt to . In the K - shot learning setting , the model is trained to learn a new task T i drawn from p ( T ) from only K samples drawn from q i and feedback L Ti generated by T i .
The loss for task T i and model f φ takes the form In K - shot reinforcement learning , K rollouts from f θ and task T i , ( x 1 , a 1 , ...
[ reference ][ reference ] . In contrast , our method explicitly trains the parameters for sensitivity on a given task distribution , allowing for extremely efficient adaptation for problems such as K - shot learning and rapid reinforcement learning in only one or a few gradient steps . section : Experimental Evaluation
An improved super-resolution algorithm through neighbor embedding with new feature selection and example training is proposed for single image super resolution reconstruction . Firstly , we take the DCT coefficients as the feature vectors , and then adaptively choose neighbors by k-means clustering algorithm .
The financial market is very fickle and investors have the difficult task of following and trying to predict the swings of the market so that their strategies result in better financial returns . With the use of Big Data and Bayesian mathematical statistics based on prior knowledge and examples of training to determine the likelihood of a hypothesis , financial news can be tracked continuously and affecting key financial indicators , Actions and assisting the investor . The objective of this work is to propose a stock price prediction process of a company listed on the Ibovespa to support decisions in the financial market , helping an investor to better buy and sell stocks using a predictive system with Big Data and Natural Processing Language ( LPN ) to collect real-time information from news sites that may influence stock prices .
Next we describe our training solution that takes advantage of these GPU implementations while still preserving the spatial pyramid pooling behaviors . subsubsection : Single - size training As in previous works , we first consider a network taking a fixed - size input ( 224 224 ) cropped from images .
This result is achieved without increasing the training time ( due to the same number of mini - batches ) and without fine - tuning . We argue that this is a promising result , given that the training task of classifying 5 K categories is a more challenging one . section : Experiments on CIFAR
A multiclass classification method by distance mapping learning network Heartbeats Arrhythmia Classification Using Probabilistic Multi-Class Support Vector Machines: A Comparative Study Margin based feature selection: An algorithmic approach for a set of attributes extrication Discriminant Analysis for Fast Multiclass Data Classification Through Regularized Kernel Function Approximation Study on the Multiclass Classifying Based on the One-class Classification Using Relative Classification Probability to Increase Accuracy of Restricted Structure Bayesian Network Classifiers Color image quality assessment with biologically inspired feature and machine learning DeepGx: Deep Learning Using Gene Expression for Cancer Classification Challenges in Data-to-Document Generation DeepWalk: online learning of social representations SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation Sequence-Level Knowledge Distillation Image Captioning and Visual Question Answering Based on Attributes and External Knowledge Brain tumor segmentation with Deep Neural Networks One-Shot Learning for Function-Specific Region Segmentation in Mouse Brain A Machine Vision-Based Gestural Interface for People With Upper Extremity Physical Impairments Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems One shot learning gesture recognition with Kinect sensor Make SVM great again with Siamese kernel for few-shot learning Visual Imitation Learning with Recurrent Siamese Networks Demonstration of PLOW: A Dialogue System for One-Shot Task Learning One-shot learning and big data with n=2 One-shot learning in the road sign problem Probabilistic Model-Agnostic Meta-Learning Matching Networks for One Shot Learning InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets Few-shot Classifier GAN Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks An efficient piecewise linear model for predicting activity of caspase-3 inhibitors Semi-supervised multi-class Adaboost by exploiting unlabeled data Training 100,000 Classes on a Single Titan X in 7 Hours or 15 Minutes with 25 Titan Xs Recognition of Isolated Handwritten Characters in Gurmukhi Script Image-Based Synthesis for Deep 3D Human Pose Estimation Brute-Force Facial Landmark Analysis With A 140, 000-Way Classifier K-SVCR. A support vector machine for multi-class classification Image analysis based real time detection of satellites reception state Still looking at people Simple Neural Networks Recurrent Pixel Embedding for Instance Grouping BB8: A Scalable, Accurate, Robust to Partial Occlusion Method for Predicting the 3D Poses of Challenging Objects without Using Depth FaceNet: A unified embedding for face recognition and clustering Improved Estimation of Target Velocity Using Multiple Model Estimation and a Dynamic Bayesian Network for a Robotic Tracker of Ocean Animals On the partition of R^n by hyperplanes Incipient fault diagnosis using support vector machines based on monitoring continuous decision functions Made-to-Order Spiking Neuron Model Equipped with a Multi-Timescale Adaptive Threshold An Investigation of Annotation Delay Compensation and Output-Associative Fusion for Multimodal Continuous Emotion Prediction Exact correlation between actual and estimated errors in discrete classification Copernicus Global Land Cover Layers - Collection 2 Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Invariant Information Clustering for Unsupervised Image Classification and Segmentation Task Agnostic Meta-Learning for Few-Shot Learning A Plug-in Method for Representation Factorization Few-shot learning for ear recognition Probabilistic Model-Agnostic Meta-Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Neural Program Meta-Induction OMG: Orthogonal Method of Grouping With Application of K-Shot Learning Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks Super-resolution algorithm through neighbor embedding with new feature selection and example training Big data and predictive computing to support financial market decisions Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition Aggregated Residual Transformations for Deep Neural Networks
a9abf37b2ed85e6fc10394f945d19a0d6080bd60 25c34888af047b4ec534874dc71fb22acb252913 bd7be579733cc096f1f02f55d099ae664aae9e4e 59e18599ca5fdc46fcd75fcfd90211d89a5ebe78 27677fa0f8079d94fe4bc51e6db59dd589a292ff f23f3451643cb02bf74fc9cc6389c459bc1c5acb 74799ddb81c019ea489ddf97fd1850ce5d9d8945 6bc5941de1af70a00b4d441ce918fb57fab8c777 79df05e9702ea5e3ddd66ca9d64f8ac2f212a132 dd6a31de072c5d908811e91e251efd0ca53f5d02 15b1c70e7873facf5f9b2c9b80fbddba4fd11dd7 543d63a83d2ea8eed037f219695dcf2e6c75b9eb c0f18e1e1b67d499f19375c907086dabe1f2eb16 6690f99cf643550d0568fc251e12628a2f162844 d334e72eea57c464b7a6f70acfcdfb56981dfd80 dc8812554b6a706541d3569278cf87b4d771a455 369de6648f18a314122c181ac215d690df321b30 8d065f521290626b478633d6ccfc099b3e1ff278 15b38f30de3be308b21abfb4c868d10c755295d5 e5f5618765668cc583b19f0f807722664c70ac44 0283738f421a0937f1d09e08d1523065042b370a f533b6c5b07671eae3e4debbdf47bba743ae1acf 474c75016daf7324cee0b354cad5f657b59c1dcf a879793dd57cbebb9f3be1018b8e7eb904ce546c 13bef3f8e89deb653437510ae9f223bc3a283750 e0d48f2053efd3e9598146f5b59bad81946ca411 1c94552e24f3b964c27d621fa0ed339f6130ed0c 88ea68c351ecca0811e8410b1099cc688560c33c db1f55b9133c2b61f9619709e7c823b84e03cb14 a2080d6a58e9967bbb674f109f22d931a077de02 a51a736e0a3878705070cd2d2c60e76a9c984059 3f8ce801bc37a20f523bd95a38c911f577c2dda2 0a1971e3560bf174ab885f0d489556da6e28438e bf738f8ee8dc3b8895bfcd84ccfd6fb2a063fc55 f4ba68a50922a52e7bcb85a424fa77330f6f491f 06f0fe16fcb8f80bf88746c5ef6d3780531918ab 94a3b65b358d0fd8d161110004ecd137a6ec557f da9ffe46055b48028af2bf156c87f2b0d1feb99a 0c253bb9aee9aa1ae7909700eda845bd3124197f 14375ab398801fa2e97a05e48043047f216e5755 4ca3b6e0d0aa68e0de2690e388307f73ea12fe67 6d5b62c22e22911c25ba286d831358dae2e2acfc
["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science","Mathematics"] ["Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science","Engineering"] ["Computer Science"] ["Computer Science"] ["Computer Science","Geography"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science","Medicine"] ["Computer Science"] ["Mathematics","Computer Science"] ["Geology","Computer Science"] ["Mathematics","Computer Science"] ["Mathematics","Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"] ["Computer Science"]
2002 2016 2017 2010 2007 2012 2010 2019 2017 2014 2017 2016 2018 2017 2019 2014 2018 2012 2018 2018 2007 2013 2012 2018 2016 2016 2018 2017 2012 2011 2018 2010 2018 2018 2003 2010 2011 2019 2018 2017 2015 2007 2013 2014 2009 2015 2010 2020 2017 2019 2018 2019 2019 2018 2017 2017 2017 2017 2012 2017 2015 2017
ICTAI VCIP ASONAM EMNLP KDD '14 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) EMNLP IEEE Transactions on Pattern Analysis and Machine Intelligence Medical Image Anal. ISBI ACM Multimedia HLT-NAACL (Demonstrations) NIPS IJCNN NeurIPS NIPS NIPS IJCNN ICML WWW (Companion Volume) ITSC ICMI 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition 2017 IEEE International Conference on Computer Vision (ICCV) 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ISRR AVEC@ACM Multimedia ICML 2019 IEEE/CVF International Conference on Computer Vision (ICCV) NeurIPS ICML ICML IEEE Transactions on Pattern Analysis and Machine Intelligence 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
https://api.semanticscholar.org/a9abf37b2ed85e6fc10394f945d19a0d6080bd60 https://api.semanticscholar.org/25c34888af047b4ec534874dc71fb22acb252913 https://api.semanticscholar.org/bd7be579733cc096f1f02f55d099ae664aae9e4e https://api.semanticscholar.org/59e18599ca5fdc46fcd75fcfd90211d89a5ebe78 https://api.semanticscholar.org/27677fa0f8079d94fe4bc51e6db59dd589a292ff https://api.semanticscholar.org/f23f3451643cb02bf74fc9cc6389c459bc1c5acb https://api.semanticscholar.org/74799ddb81c019ea489ddf97fd1850ce5d9d8945 https://api.semanticscholar.org/6bc5941de1af70a00b4d441ce918fb57fab8c777 https://www.semanticscholar.org/paper/13395213d47f78672ab4e81573f2b0fa0cfc8c6d https://www.semanticscholar.org/paper/fff114cbba4f3ba900f33da574283e3de7f26c83 https://www.semanticscholar.org/paper/ade1b1bfe2abefbcfe324499aa284db2b8fc50a6 https://www.semanticscholar.org/paper/57a10537978600fd33dcdd48922c791609a4851a https://www.semanticscholar.org/paper/6acd385b2742f65359efb99543ebfb9a0d1b850f https://www.semanticscholar.org/paper/441ff323c92331e655ce9ff896773fc00b55089a https://api.semanticscholar.org/79df05e9702ea5e3ddd66ca9d64f8ac2f212a132 https://api.semanticscholar.org/dd6a31de072c5d908811e91e251efd0ca53f5d02 https://api.semanticscholar.org/15b1c70e7873facf5f9b2c9b80fbddba4fd11dd7 https://api.semanticscholar.org/543d63a83d2ea8eed037f219695dcf2e6c75b9eb https://api.semanticscholar.org/c0f18e1e1b67d499f19375c907086dabe1f2eb16 https://api.semanticscholar.org/6690f99cf643550d0568fc251e12628a2f162844 https://api.semanticscholar.org/d334e72eea57c464b7a6f70acfcdfb56981dfd80 https://api.semanticscholar.org/dc8812554b6a706541d3569278cf87b4d771a455 https://api.semanticscholar.org/369de6648f18a314122c181ac215d690df321b30 https://www.semanticscholar.org/paper/38e2f851b705faa0d0a698ed9885bd6834440073 https://www.semanticscholar.org/paper/be1bb4e4aa1fcf70281b4bd24d8cd31c04864bb6 https://www.semanticscholar.org/paper/35da0a2001eea88486a5de677ab97868c93d0824 https://api.semanticscholar.org/8d065f521290626b478633d6ccfc099b3e1ff278 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://api.semanticscholar.org/15b38f30de3be308b21abfb4c868d10c755295d5 https://api.semanticscholar.org/e5f5618765668cc583b19f0f807722664c70ac44 https://api.semanticscholar.org/0283738f421a0937f1d09e08d1523065042b370a https://api.semanticscholar.org/f533b6c5b07671eae3e4debbdf47bba743ae1acf https://api.semanticscholar.org/474c75016daf7324cee0b354cad5f657b59c1dcf https://api.semanticscholar.org/a879793dd57cbebb9f3be1018b8e7eb904ce546c https://api.semanticscholar.org/13bef3f8e89deb653437510ae9f223bc3a283750 https://api.semanticscholar.org/e0d48f2053efd3e9598146f5b59bad81946ca411 https://api.semanticscholar.org/1c94552e24f3b964c27d621fa0ed339f6130ed0c https://api.semanticscholar.org/88ea68c351ecca0811e8410b1099cc688560c33c https://www.semanticscholar.org/paper/35502af359aa60ae8047df172e29503cfb29c3f9 https://www.semanticscholar.org/paper/fe25d4c99e39ce28107b56adaa3b5a43219a3a8a https://www.semanticscholar.org/paper/5aa26299435bdf7db874ef1640a6c3b5a4a2c394 https://api.semanticscholar.org/db1f55b9133c2b61f9619709e7c823b84e03cb14 https://api.semanticscholar.org/a2080d6a58e9967bbb674f109f22d931a077de02 https://api.semanticscholar.org/a51a736e0a3878705070cd2d2c60e76a9c984059 https://api.semanticscholar.org/3f8ce801bc37a20f523bd95a38c911f577c2dda2 https://api.semanticscholar.org/0a1971e3560bf174ab885f0d489556da6e28438e https://api.semanticscholar.org/bf738f8ee8dc3b8895bfcd84ccfd6fb2a063fc55 https://api.semanticscholar.org/f4ba68a50922a52e7bcb85a424fa77330f6f491f https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://www.semanticscholar.org/paper/0c9ae806059196007938f24d0327a4237ed6adf5 https://api.semanticscholar.org/06f0fe16fcb8f80bf88746c5ef6d3780531918ab https://api.semanticscholar.org/94a3b65b358d0fd8d161110004ecd137a6ec557f https://api.semanticscholar.org/da9ffe46055b48028af2bf156c87f2b0d1feb99a https://www.semanticscholar.org/paper/38e2f851b705faa0d0a698ed9885bd6834440073 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://api.semanticscholar.org/0c253bb9aee9aa1ae7909700eda845bd3124197f https://api.semanticscholar.org/14375ab398801fa2e97a05e48043047f216e5755 https://www.semanticscholar.org/paper/c889d6f98e6d79b89c3a6adf8a921f88fa6ba518 https://api.semanticscholar.org/4ca3b6e0d0aa68e0de2690e388307f73ea12fe67 https://api.semanticscholar.org/6d5b62c22e22911c25ba286d831358dae2e2acfc https://www.semanticscholar.org/paper/cbb19236820a96038d000dc629225d36e0b6294a https://www.semanticscholar.org/paper/f6e0856b4a9199fa968ac00da612a9407b5cb85c