Sparse Communication: Transformations and Applications

Description

This lecture by the instructor covers the evolution from sparse modeling to sparse communication, focusing on the use of sparse transformations in neural networks for natural language processing tasks. The talk explores various sparse regularization techniques, such as sparse neuron activations and structured sparsity, and their applications in machine-human communication. It delves into the concepts of sparsemax, entmax, and mixed distributions, highlighting their role in bridging the gap between discrete and continuous models. The lecture also discusses the implications of sparse communication for explainability and emergent communication in machine learning.

Official source

https://mediaspace.epfl.ch/media/0_o1kn5uvk

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Natural language processing

Natural language processing (NLP) is an interdisciplinary subfield of linguistics and computer science. It is primarily concerned with processing natural language datasets, such as text corpora or speech corpora, using either rule-based or probabilistic (i.e. statistical and, most recently, neural network-based) machine learning approaches. The goal is a computer capable of "understanding" the contents of documents, including the contextual nuances of the language within them.

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The history of natural language processing describes the advances of natural language processing (Outline of natural language processing). There is some overlap with the history of machine translation, the history of speech recognition, and the history of artificial intelligence. The history of machine translation dates back to the seventeenth century, when philosophers such as Leibniz and Descartes put forward proposals for codes which would relate words between languages.

Natural-language understanding

Natural-language understanding (NLU) or natural-language interpretation (NLI) is a subtopic of natural-language processing in artificial intelligence that deals with machine reading comprehension. Natural-language understanding is considered an AI-hard problem. There is considerable commercial interest in the field because of its application to automated reasoning, machine translation, question answering, news-gathering, text categorization, voice-activation, archiving, and large-scale content analysis.

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Sparse approximation (also known as sparse representation) theory deals with sparse solutions for systems of linear equations. Techniques for finding these solutions and exploiting them in applications have found wide use in , signal processing, machine learning, medical imaging, and more. Consider a linear system of equations , where is an underdetermined matrix and . The matrix (typically assumed to be full-rank) is referred to as the dictionary, and is a signal of interest.