Furthermore , we successfully re-use one of the decoders used in translation for segmentation . We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images , where we show significant improvements over standard semi-supervised training with autoencoding . Technically , we show that the resultant optimization problem can be efficiently solved by maximizing a variational lower-bound of the mutual information . This variational approach introduces a transformation decoder to approximate the intractable posterior of transformations , resulting in an autoencoding architecture with a pair of the representation encoder and the transformation decoder . Experiments demonstrate the proposed AVT model sets a new record for the performances on unsupervised tasks , greatly closing the performance gap to the supervised models . Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic representation . We consider autoencoding-based methods including denoising autoencoders , and context reconstruction , and evaluate the resulting embeddings on a forward prediction and a classification task . This paper addresses the problem of learning object structures in an image modeling process without supervision . We propose an autoencoding formulation to discover landmarks as explicit structural representations . The encoding module outputs landmark coordinates , whose validity is ensured by constraints that reflect the necessary properties for landmarks . Abstract Associative memories constructed and operating in the presence of big data offer an effective way to realize association mechanisms aimed at storing and recalling items . In this study , we develop a logic-driven model of two-level fuzzy associative memories augmented by autoencoding processing . It is composed of two functional modules . A suite of experimental studies is presented to quantify the performance of the proposed approach . Comparative studies are also conducted to show and quantify the advantages of the mechanisms of associative recall and storage augmented by the autoencoding process . Assuming that the observations lie in a Hilbert space X , the introduced Kernel Autoencoder ( KAE ) is the composition of mappings from vector-valued Reproducing Kernel Hilbert Spaces ( vv-RKHSs ) that minimizes the expected reconstruction error . Beyond a first extension of the autoencoding scheme to possibly infinite dimensional Hilbert spaces , KAE further allows to autoencode any kind of data by choosing X to be itself a RKHS . A theoretical analysis of the model is carried out , providing a generalization bound , and shedding light on its connection with Kernel Principal Component Analysis . To this end , an autoencoder is employed in which the goal is to identify an encoding that rebuilds the original data with maximum accuracy/ least loss . The hypothesis of this work is that such an autoencoding step provides an effective mechanism for discovering good data representations prior to the application of a data description technique , such as clustering . Both the autoencoder and the clustering steps are performed relative to a single user . It could minimize the risk of overfitting into base categories by inspecting the transformation-augmented variations at the encoded feature level . This results in the proposed FLAT ( Few-shot Learning via Autoencoding Transformations ) approach by autoencoding the applied transformations . The experiment results show the superior performances to the current state-of-the-art methods in literature . However , we note that the range of the learned context progressively increases from the lower to upper self-attention layers , whilst acoustic events often happen within short time spans in a left-to-right order . This leads to a question : for speech recognition , is a global view of the entire sequence still important for the upper self-attention layers in the encoder of Transformers ? To investigate this , we replace these self-attention layers with feed forward layers . Experiments show that the approach is competitive with state of the art graph kernels . Directions for future work include the use of alternative neural network architectures such as RNNs ; combining different receptive field sizes ; pretraining with RBMs and autoencoders ; and statistical relational models based on the ideas of the approach . section : Acknowledgments In order to overcome these issues , a variety of new concepts have been proposed in the literature , of which only a few can be named in this chapter . Unsupervised pre - training methods , such as autoencoders allow to initialize the weights well in order for backpropagation to quickly optimize them . The Rectified Linear Unit ( ReLU ) and In this section , we present an adversarial autoencoder architecture for dimensionality reduction and data visualization purposes . We will show that in these autoencoders , the adversarial regularization attaches the hidden code of similar images to each other and thus prevents the manifold fracturing problem that is typically encountered in the embeddings learnt by the autoencoders . Suppose we have a dataset with class labels and we would like to reduce the dimensionality of the dataset to , where is typically 2 or 3 for the visualization purposes . Optimising for function outputs to be persistent through spatio - temporal or non - material distortion is an idea shared by IIC with several works , including exemplars , IMSAT , proximity prediction , the denoising objective of Tagger , temporal slowness constraints , and optimising for features to be invariant to local image transformations . More broadly , the problem of modelling data transformation has received significant attention in deep learning , one example being the transforming autoencoder . section : Method However , the context-independent nature limits their representative power in rich context , hurting performance in Natural Language Processing ( NLP ) tasks . Bidirectional Encoder Representations from Transformers ( BERT ) , among other pre-trained language models , beats existing best results in eleven NLP tasks ( including sentence-level sentiment classification ) by a large margin , which makes it the new baseline of text representation . As a more challenging task , fewer applications of BERT have been observed for sentiment classification at the aspect level . Recent work has found evidence that Multilingual BERT ( mBERT ) , a transformer-based multilingual masked language model , is capable of zero-shot cross-lingual transfer , suggesting that some aspects of its representations are shared cross-lingually . To better understand this overlap , we extend recent work on finding syntactic trees in neural networks ' internal representations to the multilingual setting . In both cases , we find that BERT encodes positional information about word tokens well on its lower layers , but switches to a hierarchically-oriented encoding on higher layers . We conclude then that BERT 's representations do indeed model linguistically relevant aspects of hierarchical structure , though they do not appear to show the sharp sensitivity to hierarchical structure that is found in human processing of reflexive anaphora . In this setting , retrieving entity candidates can be particularly challenging , since many of the common linking cues such as entity alias tables and link popularity are not available . In this paper , we introduce a simple and effective two-stage approach for zero-shot linking , based on fine-tuned BERT architectures . In the first stage , we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions . The identification and mining of entities in Electronic Medical Record ( EMR ) plays an important role in medical diagnosis . This paper used the BioBERT model based on Google BERT model for automatic annotation of clinical problems , treatments and tests in EMR . This method firstly pre-trained BioBERT model on the corpus of medical related fields to convert the text into a numerical vector . We show that the default BERT model failed to outperform a simple argmax method . However , changing the default BERT tokenizer to our custom one can improve the F1 scores on our labels of interest by at least 5 % . For I-ASPECT and B-SENTIMENT , it can even increased the F1 scores by 11 % . In this work , we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions . To this end , we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering . The model is trained in an end-to-end fashion , where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question . The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention mechanisms . We describe several styles of transfer learning from auxiliary tasks , including a novel method for adapting pretrained BERT models to Twitter data . Logistic regression ties the systems together into an ensemble submitted for evaluation . The resulting instance probabilities are used for selection while being agnostic to the segmentation or attention mechanism . Our results demonstrate that auto-completion using both language and vision performs better than using only language , and that fine tuning a BERT embedding allows to efficiently rank instances in the image . In the spirit of reproducible research we make our data , models , and code available . The BERT model learns language representation that can be adapted to various tasks via pre-training using a large corpus in an unsupervised manner . This study proposes the language and action learning using multimodal BERT ( lamBERT ) model that enables the learning of language and actions by 1 ) extending the BERT model to multimodal representation and 2 ) integrating it with reinforcement learning . To verify the proposed model , an experiment is conducted in a grid environment that requires language understanding for the agent to act properly . Based on this framework , spatial Correlation-based Collaborative Medium Access Control ( CC-MAC ) protocol is described , which exploits the spatial correlation in the WSN in order to achieve efficient medium access . Furthermore , the cross-layer module ( XLM ) , which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes , is developed . The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN . Examples of topics are XML Europe , Paris , France . Topics also have different types conference , city , country , etc . and associations exist between them : XLM Europe takes places in Paris , Paris is in France . In this example , there are two types of associations " takes place in " and " is in " . We use KoBoCollect app on Android tablets from where completed forms are sent to the secure cloud server . KoBoCollect forms were developed based on paper forms using XLM language . Digitizing has several advantages : KoBo supports data of all types ( text , images , GPS ) ; such features of the KoBo form as hints , constraints , required decrease the errors during data entry ; XLM forms are easily customized and offer multilingual support ; cloud server enables multi-location data collection ; touch-screen Android tablets and simple KoBo app are easily adopted by users without special IT skills . The Total Monte Carlo ( TMC ) method was used in this study to assess the impact of Pb-206 , 207 and 208 nucleardata uncertainties on k-eff , beta-eff , coolant temperature coefficient , the coolant void worth for the ELECTRA reactor . Relatively large uncertainties were observed in the k-eff and the coolant void worth for all the isotopes with significant contribution coming from Pb-208 nuclear data . ( 10/12/2018 ) Report on the evaluation and validation of the ELECTRA WoC control concept This report summarizes the evaluation and validation of the ELECTRA Web-of-Cells concept which has been implemented for the proof of concept in selected validation environments provided by the project partners . The performed experiments have been realized in simulations and in laboratory environments and showed the feasibility of the Web-of-Cells concept and integrated functions for balancing and voltage control .
Furthermore , we successfully re-use one of the decoders used in translation for segmentation . We validate the proposed method on synthetic tasks of varying difficulty as well as on the real task of brain tumor segmentation in magnetic resonance images , where we show significant improvements over standard semi-supervised training with autoencoding .
Technically , we show that the resultant optimization problem can be efficiently solved by maximizing a variational lower-bound of the mutual information . This variational approach introduces a transformation decoder to approximate the intractable posterior of transformations , resulting in an autoencoding architecture with a pair of the representation encoder and the transformation decoder . Experiments demonstrate the proposed AVT model sets a new record for the performances on unsupervised tasks , greatly closing the performance gap to the supervised models .
Each embedding is a vector that represents four contiguous beats of music and is derived from a symbolic representation . We consider autoencoding-based methods including denoising autoencoders , and context reconstruction , and evaluate the resulting embeddings on a forward prediction and a classification task .
This paper addresses the problem of learning object structures in an image modeling process without supervision . We propose an autoencoding formulation to discover landmarks as explicit structural representations . The encoding module outputs landmark coordinates , whose validity is ensured by constraints that reflect the necessary properties for landmarks .
Abstract Associative memories constructed and operating in the presence of big data offer an effective way to realize association mechanisms aimed at storing and recalling items . In this study , we develop a logic-driven model of two-level fuzzy associative memories augmented by autoencoding processing . It is composed of two functional modules .
A suite of experimental studies is presented to quantify the performance of the proposed approach . Comparative studies are also conducted to show and quantify the advantages of the mechanisms of associative recall and storage augmented by the autoencoding process .
Assuming that the observations lie in a Hilbert space X , the introduced Kernel Autoencoder ( KAE ) is the composition of mappings from vector-valued Reproducing Kernel Hilbert Spaces ( vv-RKHSs ) that minimizes the expected reconstruction error . Beyond a first extension of the autoencoding scheme to possibly infinite dimensional Hilbert spaces , KAE further allows to autoencode any kind of data by choosing X to be itself a RKHS . A theoretical analysis of the model is carried out , providing a generalization bound , and shedding light on its connection with Kernel Principal Component Analysis .
To this end , an autoencoder is employed in which the goal is to identify an encoding that rebuilds the original data with maximum accuracy/ least loss . The hypothesis of this work is that such an autoencoding step provides an effective mechanism for discovering good data representations prior to the application of a data description technique , such as clustering . Both the autoencoder and the clustering steps are performed relative to a single user .
It could minimize the risk of overfitting into base categories by inspecting the transformation-augmented variations at the encoded feature level . This results in the proposed FLAT ( Few-shot Learning via Autoencoding Transformations ) approach by autoencoding the applied transformations . The experiment results show the superior performances to the current state-of-the-art methods in literature .
However , we note that the range of the learned context progressively increases from the lower to upper self-attention layers , whilst acoustic events often happen within short time spans in a left-to-right order . This leads to a question : for speech recognition , is a global view of the entire sequence still important for the upper self-attention layers in the encoder of Transformers ? To investigate this , we replace these self-attention layers with feed forward layers .
Experiments show that the approach is competitive with state of the art graph kernels . Directions for future work include the use of alternative neural network architectures such as RNNs ; combining different receptive field sizes ; pretraining with RBMs and autoencoders ; and statistical relational models based on the ideas of the approach . section : Acknowledgments
In order to overcome these issues , a variety of new concepts have been proposed in the literature , of which only a few can be named in this chapter . Unsupervised pre - training methods , such as autoencoders allow to initialize the weights well in order for backpropagation to quickly optimize them . The Rectified Linear Unit ( ReLU ) and
In this section , we present an adversarial autoencoder architecture for dimensionality reduction and data visualization purposes . We will show that in these autoencoders , the adversarial regularization attaches the hidden code of similar images to each other and thus prevents the manifold fracturing problem that is typically encountered in the embeddings learnt by the autoencoders . Suppose we have a dataset with class labels and we would like to reduce the dimensionality of the dataset to , where is typically 2 or 3 for the visualization purposes .
Optimising for function outputs to be persistent through spatio - temporal or non - material distortion is an idea shared by IIC with several works , including exemplars , IMSAT , proximity prediction , the denoising objective of Tagger , temporal slowness constraints , and optimising for features to be invariant to local image transformations . More broadly , the problem of modelling data transformation has received significant attention in deep learning , one example being the transforming autoencoder . section : Method
However , the context-independent nature limits their representative power in rich context , hurting performance in Natural Language Processing ( NLP ) tasks . Bidirectional Encoder Representations from Transformers ( BERT ) , among other pre-trained language models , beats existing best results in eleven NLP tasks ( including sentence-level sentiment classification ) by a large margin , which makes it the new baseline of text representation . As a more challenging task , fewer applications of BERT have been observed for sentiment classification at the aspect level .
Recent work has found evidence that Multilingual BERT ( mBERT ) , a transformer-based multilingual masked language model , is capable of zero-shot cross-lingual transfer , suggesting that some aspects of its representations are shared cross-lingually . To better understand this overlap , we extend recent work on finding syntactic trees in neural networks ' internal representations to the multilingual setting .
In both cases , we find that BERT encodes positional information about word tokens well on its lower layers , but switches to a hierarchically-oriented encoding on higher layers . We conclude then that BERT 's representations do indeed model linguistically relevant aspects of hierarchical structure , though they do not appear to show the sharp sensitivity to hierarchical structure that is found in human processing of reflexive anaphora .
In this setting , retrieving entity candidates can be particularly challenging , since many of the common linking cues such as entity alias tables and link popularity are not available . In this paper , we introduce a simple and effective two-stage approach for zero-shot linking , based on fine-tuned BERT architectures . In the first stage , we do retrieval in a dense space defined by a bi-encoder that independently embeds the mention context and the entity descriptions .
The identification and mining of entities in Electronic Medical Record ( EMR ) plays an important role in medical diagnosis . This paper used the BioBERT model based on Google BERT model for automatic annotation of clinical problems , treatments and tests in EMR . This method firstly pre-trained BioBERT model on the corpus of medical related fields to convert the text into a numerical vector .
We show that the default BERT model failed to outperform a simple argmax method . However , changing the default BERT tokenizer to our custom one can improve the F1 scores on our labels of interest by at least 5 % . For I-ASPECT and B-SENTIMENT , it can even increased the F1 scores by 11 % .
In this work , we propose a variant of the self-attention Transformer network architectures model to generate meaningful and diverse questions . To this end , we propose an easy to use model consisting of the conjunction of the Transformer decoder GPT-2 model with Transformer encoder BERT for the downstream task for question answering . The model is trained in an end-to-end fashion , where the language model is trained to produce a question-answer-aware input representation that facilitates to generate an answer focused question .
The techniques explored range from simple bag-of-ngrams classifiers to neural architectures with varied attention mechanisms . We describe several styles of transfer learning from auxiliary tasks , including a novel method for adapting pretrained BERT models to Twitter data . Logistic regression ties the systems together into an ensemble submitted for evaluation .
The resulting instance probabilities are used for selection while being agnostic to the segmentation or attention mechanism . Our results demonstrate that auto-completion using both language and vision performs better than using only language , and that fine tuning a BERT embedding allows to efficiently rank instances in the image . In the spirit of reproducible research we make our data , models , and code available .
The BERT model learns language representation that can be adapted to various tasks via pre-training using a large corpus in an unsupervised manner . This study proposes the language and action learning using multimodal BERT ( lamBERT ) model that enables the learning of language and actions by 1 ) extending the BERT model to multimodal representation and 2 ) integrating it with reinforcement learning . To verify the proposed model , an experiment is conducted in a grid environment that requires language understanding for the agent to act properly .
Based on this framework , spatial Correlation-based Collaborative Medium Access Control ( CC-MAC ) protocol is described , which exploits the spatial correlation in the WSN in order to achieve efficient medium access . Furthermore , the cross-layer module ( XLM ) , which melts common protocol layer functionalities into a cross-layer module for resource-constrained sensor nodes , is developed . The cross-layer analysis of error control in WSN is then presented to enable a comprehensive comparison of error control schemes for WSN .
Examples of topics are XML Europe , Paris , France . Topics also have different types conference , city , country , etc . and associations exist between them : XLM Europe takes places in Paris , Paris is in France . In this example , there are two types of associations " takes place in " and " is in " .
We use KoBoCollect app on Android tablets from where completed forms are sent to the secure cloud server . KoBoCollect forms were developed based on paper forms using XLM language . Digitizing has several advantages : KoBo supports data of all types ( text , images , GPS ) ; such features of the KoBo form as hints , constraints , required decrease the errors during data entry ; XLM forms are easily customized and offer multilingual support ; cloud server enables multi-location data collection ; touch-screen Android tablets and simple KoBo app are easily adopted by users without special IT skills .
The Total Monte Carlo ( TMC ) method was used in this study to assess the impact of Pb-206 , 207 and 208 nucleardata uncertainties on k-eff , beta-eff , coolant temperature coefficient , the coolant void worth for the ELECTRA reactor . Relatively large uncertainties were observed in the k-eff and the coolant void worth for all the isotopes with significant contribution coming from Pb-208 nuclear data .
( 10/12/2018 ) Report on the evaluation and validation of the ELECTRA WoC control concept This report summarizes the evaluation and validation of the ELECTRA Web-of-Cells concept which has been implemented for the proof of concept in selected validation environments provided by the project partners . The performed experiments have been realized in simulations and in laboratory environments and showed the feasibility of the Web-of-Cells concept and integrated functions for balancing and voltage control .
Towards semi-supervised segmentation via image-to-image translation AVT: Unsupervised Learning of Transformation Equivariant Representations by Autoencoding Variational Transformations Learning and Evaluating Musical Features with Deep Autoencoders Unsupervised Discovery of Object Landmarks as Structural Representations Fuzzy associative memories with autoencoding mechanisms Autoencoding any Data through Kernel Autoencoders Smart Phone User Behaviour Characterization Based on Autoencoders and Self Organizing Maps FLAT: Few-Shot Learning via Autoencoding Transformation Regularizers When Can Self-Attention Be Replaced by Feed Forward Layers? Learning Convolutional Neural Networks for Graphs Deep Learning For Smile Recognition Adversarial Autoencoders Invariant Information Clustering for Unsupervised Image Classification and Segmentation Target-Dependent Sentiment Classification With BERT Finding Universal Grammatical Relations in Multilingual BERT Open Sesame: Getting Inside BERT's Linguistic Knowledge Zero-shot Entity Linking with Dense Entity Retrieval BioBERT Based Named Entity Recognition in Electronic Medical Record Aspect and Opinion Term Extraction for Aspect Based Sentiment Analysis of Hotel Reviews Using Transfer Learning Learning to Answer by Learning to Ask: Getting the Best of GPT-2 and BERT Worlds MITRE at SemEval-2019 Task 5: Transfer Learning for Multilingual Hate Speech Detection Visual Natural Language Query Auto-Completion for Estimating Instance Probabilities lamBERT: Language and Action Learning Using Multimodal BERT Correlation-based cross-layer communication in wireless sensor networks Information management - Topic Maps visualization PW 1797 Digitizing data collection for roadside observational studies: the process and experience Uncertainty analysis of Lead cross sections on reactor safety for ELECTRA Report on the evaluation and validation of the ELECTRA WoC control concept
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