CIF: Small: Learning Signal Representations for Multiple Inference Tasks

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Rapid advances in high-performance computing and widespread availability of massive datasets are bringing about a paradigm shift in the theory and practice of signal representations, geared towards inference and learning. A signal representation is a compressed summary that only retains those features of the signal that are salient for a class of inference tasks. This project provides a comprehensive theoretical and algorithmic framework for signal representations, which is sufficiently broad to cover both the traditional types of signal representations, such as vector quantization and sparse codes, and the more modern types, inspired by recent advances in machine learning and signal processing for Big Data. Under this framework, the statistical performance and the computational complexity of signal representations are addressed in a unified manner by imposing structural constraints on the encoding map, the decoding map, and the model space of the representation, while simultaneously tailoring these objects to the class of tasks of interest. This unification leads to new theoretical and algorithmic insights into highly structured internal representations that are a key factor in recent spectacular success of deep neural networks on challenging tasks in visual, audio, and speech analytics.