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The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.

Technology: general issues --- History of engineering & technology --- supervised classification --- independent and non-identically distributed features --- analytical error probability --- empirical risk --- generalization error --- K-means clustering --- model compression --- population risk --- rate distortion theory --- vector quantization --- overfitting --- information criteria --- entropy --- model-based clustering --- merging mixture components --- component overlap --- interpretability --- time series prediction --- finite state machines --- hidden Markov models --- recurrent neural networks --- reservoir computers --- long short-term memory --- deep neural network --- information theory --- local information geometry --- feature extraction --- spiking neural network --- meta-learning --- information theoretic learning --- minimum error entropy --- artificial general intelligence --- closed-loop transcription --- linear discriminative representation --- rate reduction --- minimax game --- fairness --- HGR maximal correlation --- independence criterion --- separation criterion --- pattern dictionary --- atypicality --- Lempel–Ziv algorithm --- lossless compression --- anomaly detection --- information-theoretic bounds --- distribution and federated learning

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This Special Issue highlights the most recent research being carried out in the NLP field to discuss relative open issues, with a particular focus on both emerging approaches for language learning, understanding, production, and grounding interactively or autonomously from data in cognitive and neural systems, as well as on their potential or real applications in different domains.

Information technology industries --- Computer science --- tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein-protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks --- tourism big data --- text mining --- NLP --- deep learning --- clinical named entity recognition --- information extraction --- multitask model --- long short-term memory --- conditional random field --- relation extraction --- entity recognition --- long short-term memory network --- multi-turn chatbot --- dialogue context encoding --- WGAN-based response generation --- BERT word embedding --- text summary --- reinforce learning --- FAQ classification --- encoder-decoder neural network --- multi-level word embeddings --- BERT --- bidirectional RNN --- cloze test --- Korean dataset --- machine comprehension --- neural language model --- sentence completion --- primary healthcare --- chief complaint --- virtual medical assistant --- spoken natural language --- disease diagnosis --- medical specialist --- protein-protein interactions --- deep learning (DL) --- convolutional neural networks (CNN) --- bidirectional long short-term memory (bidirectional LSTM) --- dialogue management --- user simulation --- reward shaping --- conversation knowledge --- multi-agent reinforcement learning --- language modeling --- classification --- error probability --- error assessment --- logic error --- neural network --- LSTM --- attention mechanism --- programming education --- neural architecture search --- word ordering --- Korean syntax --- adversarial attack --- adversarial example --- sentiment classification --- dual pointer network --- context-to-entity attention --- text classification --- rule-based --- word embedding --- Doc2vec --- paraphrase identification --- encodings --- R-GCNs --- contextual features --- sentence retrieval --- TF−ISF --- BM25 --- partial match --- sequence similarity --- word to vector --- word embeddings --- antonymy detection --- polarity --- text normalization --- natural language processing --- deep neural networks --- causal encoder --- question classification --- multilingual --- convolutional neural networks --- Natural Language Processing (NLP) --- transfer learning --- open information extraction --- recurrent neural networks --- bilingual translation --- speech-to-text --- LaTeX decompilation --- word representation learning --- word2vec --- sememes --- structural information --- sentiment analysis --- zero-shot learning --- news analysis --- cross-lingual classification --- multilingual transformers --- knowledge base --- commonsense --- sememe prediction --- attention model --- ontologies --- fixing ontologies --- quick fix --- quality metrics --- online social networks --- rumor detection --- Cantonese --- XGA model --- delayed combination --- CNN dictionary --- named entity recognition --- deep learning NER --- bidirectional LSTM CRF --- CoNLL --- OntoNotes --- toxic comments --- neural networks