Compared with typical deep learning using SoftMax or logistic regression as the top layer , DEK is expected to be more generalizable to new data . Experimental results show that DEK has superior performance than typical machine learning methods in identity detection , classification , regression , dimension reduction , and transfer learning . We study the binary transfer learning problem , focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task . In particular , we consider the transfer learning setting where one does not have direct access to the source data , but rather employs the source hypotheses trained from them . Building on the literature on the best subset selection problem , we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously . This paper introduces the first PAC-Bayesian bound for the batch reinforcement learning problem with function approximation . We show how this bound can be used to perform model-selection in a transfer learning scenario . Our empirical results confirm that PAC-Bayesian policy evaluation is able to leverage prior distributions when they are informative and , unlike standard Bayesian RL approaches , ignore them when they are misleading . In this manuscript , we are targeting at proposing a kernel method to evaluate both the task relatedness and the instance similarities . The minimum description length principle MDLP is adopted which was proved effective for evaluating models in transfer learning scenario . Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in real datasets . This assumption does not hold in certain actual applications ; analysts unfamiliar with the learning strategy can be frustrated by the complicated transfer relations and the non-intuitive transfer process . This paper presents a suite of visual communication and interaction techniques to support the transfer learning process . Furthermore , a pioneering visual-assisted transfer learning methodology is proposed in the context of classification . We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models . We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points . As expected , we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models . The first data mapping algorithm combines effective methods for finding signal similarity with network-based clustering , while the second algorithm is based on finding signal motifs . The data mapping algorithms constitute the centerpiece of the transfer learning phase in our framework . We demonstrate the efficacy of the data mapping algorithms using two publicly available datasets on human activity recognition . To cut this labelling cost , new cross domain approaches have been developed where the goal is to automatically classify the polarity of an unlabelled target text set of a given domain , for example movie reviews , from a labelled source text set of another domain , such as book reviews . Language heterogeneity between source and target domain is the trickiest issue in cross-domain setting so that a preliminary transfer learning phase is generally required . The best performing techniques addressing this point are generally complex and require onerous parameter tuning each time a new source-target couple is involved . In WI systems , a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation . Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers , and hence of being used in a transfer learning context . In this work , we present a white-box analysis of this approach highlighting how it handles the challenges , the dynamic selection of references through fusion function , and its application for transfer learning . The image processing part deals with image augmentation and removal of unwanted elements , which is found to be necessary before further processing , else it will affect the output efficiency . And transfer learning part deals with features extractions and fine tuning of pre-trained VGG16 model . The validation accuracy is found of be 74.1 % and by further fine tuning is found to be 76.3 % , when tested on those dataset . Reusing the knowledge inherent in a trained neural network for a machine learning task in a related domain can provide significant improvements in terms of the time required to complete the task . In this paper , we present our experience with such a transfer learning situation . We reuse a neural network that was trained on a real world image dataset , for the task of classifying music in terms of genre , instrumentation , composer etc . The case is significant as it allows us to advance insight how in fact the client and the new vendor manage the loss of learning by doing-knowledge ( experiential knowledge ) that the client no longer has . The case suggests that several elements were especially critical in facilitating the transfer or learning including the lost experiential knowledge : modularization of work , use of external sources , joint client-vendor collaboration , and personal identities at work . These elements allowed the inter-organizational project teams to minimize the disruptions in IT services to the client 's user communities . In this paper , we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks . Much like how computer vision uses ImageNet to obtain features , which can then be transferred to other tasks , our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks . Our encoder is publicly available . It should be noted that while related in name , adversarial training ( AT ) differs from Generative Adversarial Networks ( GANs ) . GANs have already been applied to NLP tasks such as dialogue generation and transfer learning . Adversarial training also differs from adversarial evaluation , recently proposed for reading comprehension tasks . section : Related work There has been extensive prior work on domain transfer learning , see e.g. , . Recent work has focused on transferring deep neural network representations from a labeled source datasets to a target domain where labeled data is sparse or non - existent . We demonstrate existing models used for recommendation , such as MF and BPR , can all be assembled using three basic components : sampler , mapper , and optimizer . The tutorial is accompanied by a hands-on session , where we show how graph embedding can model complex systems through the multi-task learning and the cross-platform data sparsity alleviation tasks . We propose that for all situated agents , the process of task learning has many elements in common . A better understanding of these elements would be beneficial to both engineers attempting to design new agents for task learning and completion , and also to scientists seeking to better understand natural task learning . In the past decades , although some famous action datasets have been released , there still exist limitations , including the limited action categories and samples , camera views and variety of scenarios . Moreover , most of them are designed for a subset of the learning problems , such as single-view learning problem , cross-view learning problem and multi-task learning problem . In this paper , we introduce a multi-view , multi-modality benchmark dataset for human action recognition ( abbreviated to MMA ) . For both object template and candidate samples , their observation image patches are divided into multiple fragments to model the feature and spatial information at the same time . Then the state inference processing can be viewed as a multi-task learning problem , which can be solved by the accelerated proximal gradient ( APG ) method . Finally , we design a generative tracker based on the proposed model and a simple online update manner . In many applications the M “ tasks ” defined by the mappings vi ! ^ ui are not statistically independent , and it may be possible to improve the performance of the inversion if statistical inter-relationships are exploited . In this paper we address this problem within a multi-task learning setting , wherein the mapping vi ! ^ ui for each task corresponds to inferring the parameters ( here , wavelet coefficients ) associated with the desired signal ui , and a shared prior is placed across all of the M tasks . In this multi-task learning framework data from all M tasks contribute toward inferring a posterior on the hyperparameters , and once the shared prior is thereby inferred , the data from each of the M individual tasks is then employed to estimate the task-dependent wavelet coefficients . The approach of learning of multiple “ related ” tasks simultaneously has proven quite successful in practice ; however , theoretical justification for this success has remained elusive . The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “ algorithmically related ” , in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar . We offer an alternative approach , defining relatedness of tasks on the basis of similarity between the example generating distributions that underline these task . Specifically , we look into a multivariate setting where main and auxiliary series are to be forecasted for multi-step ahead . This results in an interesting multi-task learning problem formulation where the learning tasks come from future horizon of main and auxiliary series both . Our proposed method relies firstly on enumerating multiple Convolutional network architectures to balance the number of shared and non-shared layers between different time series tasks . Results suggest that random practice is beneficial for learning only one task . Key Words : contextual interference , specificity , multi-task task learning Surgical scene understanding and multi-tasking learning are crucial for image-guided robotic surgery . Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource . Our network cascades have three stages , each of which addresses one sub - task . The three stages share their features , as in traditional multi - task learning . Feature sharing greatly reduces the test - time computation , and may also improve feature learning thanks to the underlying commonality among the tasks . In fact , a popular choice for the auxiliary task is language modeling itself . This in turn is related to multi - task learning ( ) . Specialized architectures like Recurrent Highway Networks ( ) and NAS ( ) have been successfully used to achieve competitive performance in language modeling . Multi - Task Learning as Multi - Objective Optimization section : Abstract We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions . We apply our method to a variety of multi - task deep learning problems including digit classification , scene understanding ( joint semantic segmentation , instance segmentation , and depth estimation ) , and multilabel classification . Our method produces higher - performing models than recent multi - task learning formulations or per - task training . In this paper , we decompose the problem of MRC with unanswerable questions into three sub - tasks : answer pointer , no - answer pointer , and answer verifier . Since these three sub - tasks are highly related , we regard the MRC with unanswerable questions as a multi - task learning problem by sharing some meta - knowledge . We propose the U - Net to incorporate these three sub - tasks into a unified model : 1 ) an answer pointer to predict a candidate answer span for a question ; 2 ) a no - answer pointer to avoid selecting any text span when a question has no answer ; and 3 ) an answer verifier to determine the probability of the “ unanswerability ” of a question with candidate answer information . Prior work on many - task NLP such as hashimoto2016joint uses complicated architectures and training algorithms . Our result shows that simple parameter sharing can be enough for effective many - task learning when the model is big and trained on a large amount of data . Interestingly , multi - task learning works better in conjunction with CVT than with ELMo . During the training stage , we supervise the i d - feat with the proposed OIM loss function . Together with other loss functions for detection , the whole net is jointly trained in a multi - task learning manner , rather than using the alternative optimizations in . subsection : Online Instance Matching Loss Furthermore , we extend the iterative error feedback model to model higher - order temporal dependencies ( Figure [ reference ] ) . To handle occlusions , we formulate our model with a multi - task learning objective . Second , we introduce a new dataset of 100 K depth images with pixel - wise body part labels and 3D human joint locations . On the other hand , we extend the auxiliary classifier of the discriminator to generate probability distributions over labels for all datasets . Then , we train the model in a multi - task learning setting , where the discriminator tries to minimize only the classification error associated to the known label . For example , when training with images in CelebA , the discriminator minimizes only classification errors for labels related to CelebA attributes , and not facial expressions related to RaFD . In general , the line between " explicit " and " implicit " ensembling , like dropout [ reference ] or temporal ensembling [ reference ] , is more fuzzy . As we noted earlier our multi - task learning setup can be seen as a form of self - ensembling . Multi - task learning in NLP Neural networks are particularly well - suited for MTL allowing for parameter sharing [ reference ] . Recent NLP conferences witnessed a " tsunami " of deep learning papers [ reference ] , followed by what we call a multi - task learning " wave " : MTL has been successfully applied to a wide range of NLP tasks [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] . In this paper , we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification . We first design a new auxiliary task based on sentiment scores of domain-independent words . We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains . Most Semantic Role Labeling ( SRL ) approaches are supervised methods which require a significant amount of annotated corpus , and the annotation requires linguistic expertise . In this paper , we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition ( ER ) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL . We evaluate our approach on Indonesian conversational dataset . virtual machine technology as an effective means of auxiliary teaching has been widely used in computer teaching , using the virtual machine to assist the computer teaching , we can make full use of resources , develop students ' horizons , solve the existing teaching and training environment can not meet the demand of the problem . This paper introduces the VMware using the virtual machine software for the application of network course teaching . The characteristics of Matlab software were introduced , and using its powerful function of data processing , graphical visualization and rich toolboxes and toolbox functions , the advantage of Matlab in auxiliary teaching was discussed in this paper . The results showed that integrating Matlab software into teaching , the teaching effect of the numerical analysis course can be improved . In typical classification problems , high level concept features provided by a domain expert are usually available during classifier training but not during its deployment . We address this problem from a multitask learning ( MTL ) perspective by treating these features as auxiliary learning tasks . Previous efforts in MTL have mostly assumed that all tasks have the same input space . With the recent success of deep learning-based methods in computer vision , several neural network approaches have been proposed for fully automatic RSD prediction based solely on visual data from the endoscopic camera . We investigate whether RSD prediction can be improved using unsupervised temporal video segmentation as an auxiliary learning task . As opposed to previous work , which presented supervised surgical phase recognition as auxiliary task , we avoid the need for manual annotations by proposing a similar but unsupervised learning objective which clusters video sequences into temporally coherent segments . The invention provides a limb rehabilitation device which comprises a rotating mechanism , a stretching and contracting mechanism and a movable arm which are connected with one another ; in the work state , a rotating arm in the rotating mechanism is driven by a rotation drive device to rotate around a shaft , a stretching and contracting arm is driven by a stretching and contracting drive device to stretch and contract to drive a movable arm to rotate relative to the rotating arm , and the rotating arm , the movable arm and the stretching and contracting arm form a triangular structure . The limb rehabilitation device is simple and stable in structure , low in cost , high in control precision and convenient to use , and rehabilitation personnel can conduct passive training , active training and auxiliary training with the rehabilitation device under various environments . Specifically , to promote the awareness of tail-classes , a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor . For the feature extractor , we also introduce an auxiliary training task , which is to train a classifier under the regular random sampling scheme . In this way , the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue . This paper describes the assessment methods of current environmental planning and implementation of research and development , such as comparative analysis , cost-benefit analysis , environment and economy forecasting analysis methods , from the application scope , validity , the advantages and disadvantages and other aspects making comparative analysis and mainly introduced the use of environment and economy forecasting analysis methods . The results showed that at present the most widely used is the comparative analysis , cost-benefit analysis method is highly targeted , and environmental economic forecasting and analysis method , although more advanced science , but the use is limited , can be used as auxiliary assessment . This text from flow basic conception of media , involve flow about agreement and three major mainstream performance person who compare respect of media explain . Explained in detail finally that application in auxiliary education of the computer of the media flows . And setting up of the campus network video request system of the media flows on the basis of Web . This hierarchical knowledge expression with multiknowledge-unit includes the framework , production rules , basic facts and fuzzy knowledge . in its knowledge acquisition system there included the correspondent modules for knowledge expression , KB integrity inspection and auxiliary knowledge acquisition . Its reasoning is guided by framework inference , with rule-inference as its strategy . Specifically , we explicitly decompose the generation process as follows : ( 1 ) generate image channels , ( 2 ) correlate image channels , ( 3 ) concatenate image channels into the whole image . Based on these operations , we not only perform a basic adversarial learning task for generating images , but also construct an auxiliary self-supervised learning task for further regularizing generation procedures . Experimental results demonstrate that the proposed method can improve image generation compared with representative methods and possess capabilities of image colorization and image texturization . Our system first builds a semantic tree structure based on sentence parsing , aimed at aligning textual words and image regions for accurate analysis . Next , our system learns a robust joint visual-textual semantic representation by incorporating 1 ) an attention mechanism with LSTM ( long short term memory ) and 2 ) an auxiliary semantic learning task . Extensive experimental results on several known data sets show that our method outperforms existing the state-of-the-art joint models in sentiment analysis . Many of them have get used to this platform and been indispensable to it . On the basis of the features and functions of Wechat , taking the course teaching of Management Information System as an example , this paper discussed the effect and application pattern of Wechat in the auxiliary teaching process . Although motivated by language modeling , PDR can also be applied to seq2seq models with shared input - output vocabularies , such as those used for text summarization and neural machine translation ( with byte pair encoding of words ) ( ) . Regularizing the training of an LSTM by combining the main objective function with auxiliary tasks has been successfully applied to several tasks in NLP ( ) . In fact , a popular choice for the auxiliary task is language modeling itself . In the aspect of passage selection , r3 introduced a pipelined approach that rank the passages first and then read the selected passages for answering questions . snet treats the passage ranking as an auxiliary task that can be trained jointly with the reading comprehension model . Actually , the target of our answer verification is very similar to that of the passage selection , while we pay more attention to the answer content and the answer verification process . Details about how the RND performance was obtained are avaiable in the Appendix . subsection : Evaluating the Impact of the Auxiliary Task While the results depicted in Table 3 allow us to clearly see the benefit of using an exploration bonus derived from the SR , they do not inform us about the impact of the auxiliary task in the results . In mitigating the computational costs associated with DCNNs , the proposed model design shares a significant amount of convolutional operations between the two branches , streamlining the model and optimizing for efficiency in terms of memory and inference latency . Secondly , a transfer learning regime is explored in demonstrating how such siamese DCNNs can be efficiently trained for high convergence rates without extensive manual dataset labelling . The resulting siamese framework combines semantic scene understanding with orientation estimation towards predicting collision-free and optimal navigation paths . Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning ; however , it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications . We propose the use of movement pruning , a simple , deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning . However , does knowledge transfer from approximate models always help to learn a better policy ? Despite numerous empirical studies of transfer reinforcement learning , an answer to this question is still elusive . In this paper , we provide a strong negative result , showing that even the full knowledge of an approximate model may not help reduce the number of samples for learning an accurate policy of the true model . Specifically , in deep generation network , we used similarity measurement to improved optimization , which generate high quality fake image and laid the further foundation of data . Then , in transfer learning detection , we used multi-layer adaptation and multi-core MMD to fine-tune and frozen pre-trained model , prevent the problem of over-fit and improve the generalization and stability of the system . And we combined the methods of regional suggestion and regression for target detection to guarantee precision of detection . Though word embeddings and topics are complementary representations , several past works have only used pre-trained word embeddings in ( neural ) topic modeling to address data sparsity problem in short text or small collection of documents . However , no prior work has employed ( pre-trained latent ) topics in transfer learning paradigm . In this paper , we propose an approach to ( 1 ) perform knowledge transfer using latent topics obtained from a large source corpus , and ( 2 ) jointly transfer knowledge via the two representations ( or views ) in neural topic modeling to improve topic quality , better deal with polysemy and data sparsity issues in a target corpus . In this paper , we present a novel approach to transfer learning in which all available source policies are exploited to help learn several related new tasks . Moreover , our approach is compatible with tasks that differ by their transition functions , which is rarely considered in the transfer reinforcement learning literature . Our in-depth empirical evaluation demonstrates that our approach significantly improves sample-efficiency . In this paper , we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context . Specifically , we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy .
Compared with typical deep learning using SoftMax or logistic regression as the top layer , DEK is expected to be more generalizable to new data . Experimental results show that DEK has superior performance than typical machine learning methods in identity detection , classification , regression , dimension reduction , and transfer learning .
We study the binary transfer learning problem , focusing on how to select sources from a large pool and how to combine them to yield a good performance on a target task . In particular , we consider the transfer learning setting where one does not have direct access to the source data , but rather employs the source hypotheses trained from them . Building on the literature on the best subset selection problem , we propose an efficient algorithm that selects relevant source hypotheses and feature dimensions simultaneously .
This paper introduces the first PAC-Bayesian bound for the batch reinforcement learning problem with function approximation . We show how this bound can be used to perform model-selection in a transfer learning scenario . Our empirical results confirm that PAC-Bayesian policy evaluation is able to leverage prior distributions when they are informative and , unlike standard Bayesian RL approaches , ignore them when they are misleading .
In this manuscript , we are targeting at proposing a kernel method to evaluate both the task relatedness and the instance similarities . The minimum description length principle MDLP is adopted which was proved effective for evaluating models in transfer learning scenario . Extensive experiments show the effectiveness of our algorithm in terms of the classification accuracy in real datasets .
This assumption does not hold in certain actual applications ; analysts unfamiliar with the learning strategy can be frustrated by the complicated transfer relations and the non-intuitive transfer process . This paper presents a suite of visual communication and interaction techniques to support the transfer learning process . Furthermore , a pioneering visual-assisted transfer learning methodology is proposed in the context of classification .
We provide a systematic review of transfer learning application for scene classification using different datasets and different deep-learning models . We evaluate how the specialization of convolutional neural network models affects the transfer learning process by splitting original models in different points . As expected , we find the choice of hyperparameters used to train the model has a significant influence on the final performance of the models .
The first data mapping algorithm combines effective methods for finding signal similarity with network-based clustering , while the second algorithm is based on finding signal motifs . The data mapping algorithms constitute the centerpiece of the transfer learning phase in our framework . We demonstrate the efficacy of the data mapping algorithms using two publicly available datasets on human activity recognition .
To cut this labelling cost , new cross domain approaches have been developed where the goal is to automatically classify the polarity of an unlabelled target text set of a given domain , for example movie reviews , from a labelled source text set of another domain , such as book reviews . Language heterogeneity between source and target domain is the trickiest issue in cross-domain setting so that a preliminary transfer learning phase is generally required . The best performing techniques addressing this point are generally complex and require onerous parameter tuning each time a new source-target couple is involved .
In WI systems , a single model is trained to perform signature verification for all writers from a dissimilarity space generated by the dichotomy transformation . Among the advantages of this framework is its scalability to deal with some of these challenges and its ease in managing new writers , and hence of being used in a transfer learning context . In this work , we present a white-box analysis of this approach highlighting how it handles the challenges , the dynamic selection of references through fusion function , and its application for transfer learning .
The image processing part deals with image augmentation and removal of unwanted elements , which is found to be necessary before further processing , else it will affect the output efficiency . And transfer learning part deals with features extractions and fine tuning of pre-trained VGG16 model . The validation accuracy is found of be 74.1 % and by further fine tuning is found to be 76.3 % , when tested on those dataset .
Reusing the knowledge inherent in a trained neural network for a machine learning task in a related domain can provide significant improvements in terms of the time required to complete the task . In this paper , we present our experience with such a transfer learning situation . We reuse a neural network that was trained on a real world image dataset , for the task of classifying music in terms of genre , instrumentation , composer etc .
The case is significant as it allows us to advance insight how in fact the client and the new vendor manage the loss of learning by doing-knowledge ( experiential knowledge ) that the client no longer has . The case suggests that several elements were especially critical in facilitating the transfer or learning including the lost experiential knowledge : modularization of work , use of external sources , joint client-vendor collaboration , and personal identities at work . These elements allowed the inter-organizational project teams to minimize the disruptions in IT services to the client 's user communities .
In this paper , we show how universal sentence representations trained using the supervised data of the Stanford Natural Language Inference datasets can consistently outperform unsupervised methods like SkipThought vectors on a wide range of transfer tasks . Much like how computer vision uses ImageNet to obtain features , which can then be transferred to other tasks , our work tends to indicate the suitability of natural language inference for transfer learning to other NLP tasks . Our encoder is publicly available .
It should be noted that while related in name , adversarial training ( AT ) differs from Generative Adversarial Networks ( GANs ) . GANs have already been applied to NLP tasks such as dialogue generation and transfer learning . Adversarial training also differs from adversarial evaluation , recently proposed for reading comprehension tasks .
section : Related work There has been extensive prior work on domain transfer learning , see e.g. , . Recent work has focused on transferring deep neural network representations from a labeled source datasets to a target domain where labeled data is sparse or non - existent .
We demonstrate existing models used for recommendation , such as MF and BPR , can all be assembled using three basic components : sampler , mapper , and optimizer . The tutorial is accompanied by a hands-on session , where we show how graph embedding can model complex systems through the multi-task learning and the cross-platform data sparsity alleviation tasks .
We propose that for all situated agents , the process of task learning has many elements in common . A better understanding of these elements would be beneficial to both engineers attempting to design new agents for task learning and completion , and also to scientists seeking to better understand natural task learning .
In the past decades , although some famous action datasets have been released , there still exist limitations , including the limited action categories and samples , camera views and variety of scenarios . Moreover , most of them are designed for a subset of the learning problems , such as single-view learning problem , cross-view learning problem and multi-task learning problem . In this paper , we introduce a multi-view , multi-modality benchmark dataset for human action recognition ( abbreviated to MMA ) .
For both object template and candidate samples , their observation image patches are divided into multiple fragments to model the feature and spatial information at the same time . Then the state inference processing can be viewed as a multi-task learning problem , which can be solved by the accelerated proximal gradient ( APG ) method . Finally , we design a generative tracker based on the proposed model and a simple online update manner .
In many applications the M “ tasks ” defined by the mappings vi ! ^ ui are not statistically independent , and it may be possible to improve the performance of the inversion if statistical inter-relationships are exploited . In this paper we address this problem within a multi-task learning setting , wherein the mapping vi ! ^ ui for each task corresponds to inferring the parameters ( here , wavelet coefficients ) associated with the desired signal ui , and a shared prior is placed across all of the M tasks . In this multi-task learning framework data from all M tasks contribute toward inferring a posterior on the hyperparameters , and once the shared prior is thereby inferred , the data from each of the M individual tasks is then employed to estimate the task-dependent wavelet coefficients .
The approach of learning of multiple “ related ” tasks simultaneously has proven quite successful in practice ; however , theoretical justification for this success has remained elusive . The starting point for previous work on multiple task learning has been that the tasks to be learned jointly are somehow “ algorithmically related ” , in the sense that the results of applying a specific learning algorithm to these tasks are assumed to be similar . We offer an alternative approach , defining relatedness of tasks on the basis of similarity between the example generating distributions that underline these task .
Specifically , we look into a multivariate setting where main and auxiliary series are to be forecasted for multi-step ahead . This results in an interesting multi-task learning problem formulation where the learning tasks come from future horizon of main and auxiliary series both . Our proposed method relies firstly on enumerating multiple Convolutional network architectures to balance the number of shared and non-shared layers between different time series tasks .
Results suggest that random practice is beneficial for learning only one task . Key Words : contextual interference , specificity , multi-task task learning
Surgical scene understanding and multi-tasking learning are crucial for image-guided robotic surgery . Training a real-time robotic system for the detection and segmentation of high-resolution images provides a challenging problem with the limited computational resource .
Our network cascades have three stages , each of which addresses one sub - task . The three stages share their features , as in traditional multi - task learning . Feature sharing greatly reduces the test - time computation , and may also improve feature learning thanks to the underlying commonality among the tasks .
In fact , a popular choice for the auxiliary task is language modeling itself . This in turn is related to multi - task learning ( ) . Specialized architectures like Recurrent Highway Networks ( ) and NAS ( ) have been successfully used to achieve competitive performance in language modeling .
Multi - Task Learning as Multi - Objective Optimization section : Abstract
We further prove that optimizing this upper bound yields a Pareto optimal solution under realistic assumptions . We apply our method to a variety of multi - task deep learning problems including digit classification , scene understanding ( joint semantic segmentation , instance segmentation , and depth estimation ) , and multilabel classification . Our method produces higher - performing models than recent multi - task learning formulations or per - task training .
In this paper , we decompose the problem of MRC with unanswerable questions into three sub - tasks : answer pointer , no - answer pointer , and answer verifier . Since these three sub - tasks are highly related , we regard the MRC with unanswerable questions as a multi - task learning problem by sharing some meta - knowledge . We propose the U - Net to incorporate these three sub - tasks into a unified model : 1 ) an answer pointer to predict a candidate answer span for a question ; 2 ) a no - answer pointer to avoid selecting any text span when a question has no answer ; and 3 ) an answer verifier to determine the probability of the “ unanswerability ” of a question with candidate answer information .
Prior work on many - task NLP such as hashimoto2016joint uses complicated architectures and training algorithms . Our result shows that simple parameter sharing can be enough for effective many - task learning when the model is big and trained on a large amount of data . Interestingly , multi - task learning works better in conjunction with CVT than with ELMo .
During the training stage , we supervise the i d - feat with the proposed OIM loss function . Together with other loss functions for detection , the whole net is jointly trained in a multi - task learning manner , rather than using the alternative optimizations in . subsection : Online Instance Matching Loss
Furthermore , we extend the iterative error feedback model to model higher - order temporal dependencies ( Figure [ reference ] ) . To handle occlusions , we formulate our model with a multi - task learning objective . Second , we introduce a new dataset of 100 K depth images with pixel - wise body part labels and 3D human joint locations .
On the other hand , we extend the auxiliary classifier of the discriminator to generate probability distributions over labels for all datasets . Then , we train the model in a multi - task learning setting , where the discriminator tries to minimize only the classification error associated to the known label . For example , when training with images in CelebA , the discriminator minimizes only classification errors for labels related to CelebA attributes , and not facial expressions related to RaFD .
In general , the line between " explicit " and " implicit " ensembling , like dropout [ reference ] or temporal ensembling [ reference ] , is more fuzzy . As we noted earlier our multi - task learning setup can be seen as a form of self - ensembling . Multi - task learning in NLP Neural networks are particularly well - suited for MTL allowing for parameter sharing [ reference ] . Recent NLP conferences witnessed a " tsunami " of deep learning papers [ reference ] , followed by what we call a multi - task learning " wave " : MTL has been successfully applied to a wide range of NLP tasks [ reference ][ reference ][ reference ][ reference ][ reference ][ reference ][ reference ] .
In this paper , we study domain adaptation with a state-of-the-art hierarchical neural network for document-level sentiment classification . We first design a new auxiliary task based on sentiment scores of domain-independent words . We then propose two neural network architectures to respectively induce document embeddings and sentence embeddings that work well for different domains .
Most Semantic Role Labeling ( SRL ) approaches are supervised methods which require a significant amount of annotated corpus , and the annotation requires linguistic expertise . In this paper , we propose a Multi-Task Active Learning framework for Semantic Role Labeling with Entity Recognition ( ER ) as the auxiliary task to alleviate the need for extensive data and use additional information from ER to help SRL . We evaluate our approach on Indonesian conversational dataset .
virtual machine technology as an effective means of auxiliary teaching has been widely used in computer teaching , using the virtual machine to assist the computer teaching , we can make full use of resources , develop students ' horizons , solve the existing teaching and training environment can not meet the demand of the problem . This paper introduces the VMware using the virtual machine software for the application of network course teaching .
The characteristics of Matlab software were introduced , and using its powerful function of data processing , graphical visualization and rich toolboxes and toolbox functions , the advantage of Matlab in auxiliary teaching was discussed in this paper . The results showed that integrating Matlab software into teaching , the teaching effect of the numerical analysis course can be improved .
In typical classification problems , high level concept features provided by a domain expert are usually available during classifier training but not during its deployment . We address this problem from a multitask learning ( MTL ) perspective by treating these features as auxiliary learning tasks . Previous efforts in MTL have mostly assumed that all tasks have the same input space .
With the recent success of deep learning-based methods in computer vision , several neural network approaches have been proposed for fully automatic RSD prediction based solely on visual data from the endoscopic camera . We investigate whether RSD prediction can be improved using unsupervised temporal video segmentation as an auxiliary learning task . As opposed to previous work , which presented supervised surgical phase recognition as auxiliary task , we avoid the need for manual annotations by proposing a similar but unsupervised learning objective which clusters video sequences into temporally coherent segments .
The invention provides a limb rehabilitation device which comprises a rotating mechanism , a stretching and contracting mechanism and a movable arm which are connected with one another ; in the work state , a rotating arm in the rotating mechanism is driven by a rotation drive device to rotate around a shaft , a stretching and contracting arm is driven by a stretching and contracting drive device to stretch and contract to drive a movable arm to rotate relative to the rotating arm , and the rotating arm , the movable arm and the stretching and contracting arm form a triangular structure . The limb rehabilitation device is simple and stable in structure , low in cost , high in control precision and convenient to use , and rehabilitation personnel can conduct passive training , active training and auxiliary training with the rehabilitation device under various environments .
Specifically , to promote the awareness of tail-classes , a class-balanced sampling scheme is utilised for training both the classifier and the feature extractor . For the feature extractor , we also introduce an auxiliary training task , which is to train a classifier under the regular random sampling scheme . In this way , the feature extractor is jointly trained from both sampling strategies and thus can take advantage of all training data and avoid the over-fitting issue .
This paper describes the assessment methods of current environmental planning and implementation of research and development , such as comparative analysis , cost-benefit analysis , environment and economy forecasting analysis methods , from the application scope , validity , the advantages and disadvantages and other aspects making comparative analysis and mainly introduced the use of environment and economy forecasting analysis methods . The results showed that at present the most widely used is the comparative analysis , cost-benefit analysis method is highly targeted , and environmental economic forecasting and analysis method , although more advanced science , but the use is limited , can be used as auxiliary assessment .
This text from flow basic conception of media , involve flow about agreement and three major mainstream performance person who compare respect of media explain . Explained in detail finally that application in auxiliary education of the computer of the media flows . And setting up of the campus network video request system of the media flows on the basis of Web .
This hierarchical knowledge expression with multiknowledge-unit includes the framework , production rules , basic facts and fuzzy knowledge . in its knowledge acquisition system there included the correspondent modules for knowledge expression , KB integrity inspection and auxiliary knowledge acquisition . Its reasoning is guided by framework inference , with rule-inference as its strategy .
Specifically , we explicitly decompose the generation process as follows : ( 1 ) generate image channels , ( 2 ) correlate image channels , ( 3 ) concatenate image channels into the whole image . Based on these operations , we not only perform a basic adversarial learning task for generating images , but also construct an auxiliary self-supervised learning task for further regularizing generation procedures . Experimental results demonstrate that the proposed method can improve image generation compared with representative methods and possess capabilities of image colorization and image texturization .
Our system first builds a semantic tree structure based on sentence parsing , aimed at aligning textual words and image regions for accurate analysis . Next , our system learns a robust joint visual-textual semantic representation by incorporating 1 ) an attention mechanism with LSTM ( long short term memory ) and 2 ) an auxiliary semantic learning task . Extensive experimental results on several known data sets show that our method outperforms existing the state-of-the-art joint models in sentiment analysis .
Many of them have get used to this platform and been indispensable to it . On the basis of the features and functions of Wechat , taking the course teaching of Management Information System as an example , this paper discussed the effect and application pattern of Wechat in the auxiliary teaching process .
Although motivated by language modeling , PDR can also be applied to seq2seq models with shared input - output vocabularies , such as those used for text summarization and neural machine translation ( with byte pair encoding of words ) ( ) . Regularizing the training of an LSTM by combining the main objective function with auxiliary tasks has been successfully applied to several tasks in NLP ( ) . In fact , a popular choice for the auxiliary task is language modeling itself .
In the aspect of passage selection , r3 introduced a pipelined approach that rank the passages first and then read the selected passages for answering questions . snet treats the passage ranking as an auxiliary task that can be trained jointly with the reading comprehension model . Actually , the target of our answer verification is very similar to that of the passage selection , while we pay more attention to the answer content and the answer verification process .
Details about how the RND performance was obtained are avaiable in the Appendix . subsection : Evaluating the Impact of the Auxiliary Task While the results depicted in Table 3 allow us to clearly see the benefit of using an exploration bonus derived from the SR , they do not inform us about the impact of the auxiliary task in the results .
In mitigating the computational costs associated with DCNNs , the proposed model design shares a significant amount of convolutional operations between the two branches , streamlining the model and optimizing for efficiency in terms of memory and inference latency . Secondly , a transfer learning regime is explored in demonstrating how such siamese DCNNs can be efficiently trained for high convergence rates without extensive manual dataset labelling . The resulting siamese framework combines semantic scene understanding with orientation estimation towards predicting collision-free and optimal navigation paths .
Magnitude pruning is a widely used strategy for reducing model size in pure supervised learning ; however , it is less effective in the transfer learning regime that has become standard for state-of-the-art natural language processing applications . We propose the use of movement pruning , a simple , deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning .
However , does knowledge transfer from approximate models always help to learn a better policy ? Despite numerous empirical studies of transfer reinforcement learning , an answer to this question is still elusive . In this paper , we provide a strong negative result , showing that even the full knowledge of an approximate model may not help reduce the number of samples for learning an accurate policy of the true model .
Specifically , in deep generation network , we used similarity measurement to improved optimization , which generate high quality fake image and laid the further foundation of data . Then , in transfer learning detection , we used multi-layer adaptation and multi-core MMD to fine-tune and frozen pre-trained model , prevent the problem of over-fit and improve the generalization and stability of the system . And we combined the methods of regional suggestion and regression for target detection to guarantee precision of detection .
Though word embeddings and topics are complementary representations , several past works have only used pre-trained word embeddings in ( neural ) topic modeling to address data sparsity problem in short text or small collection of documents . However , no prior work has employed ( pre-trained latent ) topics in transfer learning paradigm . In this paper , we propose an approach to ( 1 ) perform knowledge transfer using latent topics obtained from a large source corpus , and ( 2 ) jointly transfer knowledge via the two representations ( or views ) in neural topic modeling to improve topic quality , better deal with polysemy and data sparsity issues in a target corpus .
In this paper , we present a novel approach to transfer learning in which all available source policies are exploited to help learn several related new tasks . Moreover , our approach is compatible with tasks that differ by their transition functions , which is rarely considered in the transfer reinforcement learning literature . Our in-depth empirical evaluation demonstrates that our approach significantly improves sample-efficiency .
In this paper , we study a transfer reinforcement learning problem where the state transitions and rewards are affected by the environmental context . Specifically , we consider a demonstrator agent that has access to a context-aware policy and can generate transition and reward data based on that policy .
Deep Embedding Kernel Image Analysis and Processing — ICIAP 2015 PAC-Bayesian Policy Evaluation for Reinforcement Learning Kernel methods for transfer learning to avoid negative transfer A visual analytical approach for transfer learning in classification Convolutional Neural Network for Remote-Sensing Scene Classification: Transfer Learning Analysis A Signal-Level Transfer Learning Framework for Autonomous Reconfiguration of Wearable Systems Markov chain based method for in-domain and cross-domain sentiment classification A white-box analysis on the writer-independent dichotomy transformation applied to offline handwritten signature verification Dermatological Diseases Classification using Image Processing and Deep Neural Network Transfer learning in neural networks: an experience report Changing IT Providers in Public Sector Outsourcing: Managing the Loss of Experiential Knowledge Supervised Learning of Universal Sentence Representations from Natural Language Inference Data Robust Multilingual Part-of-Speech Tagging via Adversarial Training Adversarial Discriminative Domain Adaptation SMORe: modularize graph embedding for recommendation An agent-independent task learning framework MMA: a multi-view and multi-modality benchmark dataset for human action recognition Object tracking via fragment-based multi-task sparse state inference Multi-Task Compressive Sensing Exploiting Task Relatedness for Mulitple Task Learning Multi-step Forecasting via Multi-task Learning Single Task Versus Multi-task Learning AP-MTL: Attention Pruned Multi-task Learning Model for Real-time Instrument Detection and Segmentation in Robot-assisted Surgery Instance-Aware Semantic Segmentation via Multi-task Network Cascades Multi-Task Learning as Multi-Objective Optimization U-Net: Machine Reading Comprehension with Unanswerable Questions Semi-Supervised Sequence Modeling with Cross-View Training Joint Detection and Identification Feature Learning for Person Search Towards Viewpoint Invariant 3D Human Pose Estimation StarGAN: Unified Generative Adversarial Networks for Multi-domain Image-to-Image Translation Strong Baselines for Neural Semi-supervised Learning under Domain Shift Leveraging Auxiliary Tasks for Document-Level Cross-Domain Sentiment Classification Multi-Task Active Learning for Neural Semantic Role Labeling on Low Resource Conversational Corpus Virtual Machine Technology Application in the Network Teaching On the Numerical Optimization Analysis of Teaching by Using Matlab Software Assisting Main Task Learning by Heterogeneous Auxiliary Tasks with Applications to Skin Cancer Screening Unsupervised Temporal Video Segmentation as an Auxiliary Task for Predicting the Remaining Surgery Duration Limb rehabilitation device and method for conducting rehabilitation training with same To Balance or Not to Balance: A Simple-yet-Effective Approach for Learning with Long-Tailed Distributions Quality Analysis of Evaluation Methods of Environmental Planning Implementation Network Streaming Media and Its Application The Development of Expert System Tool for Manufacturing Process Planning Self-supervised GAN for Image Generation by Correlating Image Channels Robust Visual-Textual Sentiment Analysis: When Attention meets Tree-structured Recursive Neural Networks Using the Public Platform of Wechat to Realize Auxiliary Teaching Multi-Passage Machine Reading Comprehension with Cross-Passage Answer Verification Autonomous Indoor Robot Navigation via Siamese Deep Convolutional Neural Network Movement Pruning: Adaptive Sparsity by Fine-Tuning Does Knowledge Transfer Always Help to Learn a Better Policy? Shipwrecks Detection Based on Deep Generation Network and Transfer Learning with Small Amount of Sonar Images Multi-view and Multi-source Transfers in Neural Topic Modeling with Pretrained Topic and Word Embeddings Transfer Reinforcement Learning across Environment Dynamics with Multiple Advisors Transfer Reinforcement Learning under Unobserved Contextual Information
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Image Analysis and Processing — ICIAP 2015 KDIR CASCON HICSS EMNLP NAACL-HLT 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) RecSys COLT BigData 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) NeurIPS ArXiv EMNLP 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) ECCV 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition ACL IJCNLP(1) AISTATS PCM (2) ACM Multimedia ACL BNAIC/BENELEARN
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