Supervised Sparse Analysis and Synthesis Operators

By Pablo Sprechmann, Roee Litman, Tal Ben Yakar, A. Bronstein, Guillermo Sapiro. 2013

50 citations recorded by the source corpus.

Subfields: Representation & Self-Supervised Learning

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In Representation & Self-Supervised Learning

Citations in the corpus

Learning to Optimize Neural Nets (2017, background): “A different line of work (Gregor & LeCun, 2010; Sprechmann et al., 2013) parameterizes intermediate operands of special-purpose solvers for a class of optimization problems that arise in sparse coding and learns them using supervised learning.”
Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures (2014, methodology): “Both sparse coding [7, 8], and non-negative matrix factorization [9, 10] have been addressed using unfolding and back-propagation or other discriminative optimization methods.”
Learning to Optimize: Training Deep Neural Networks for Interference Management (2017, methodology): “The authors of [12], [13] applied similar ideas for nonnegative matrix factorization and the alternating direction method of multipliers (ADMM), respectively.”
Learning to optimize: Training deep neural networks for wireless resource management (2017, methodology): “The authors of [19], [20] applied similar ideas for nonnegative matrix factorization and the alternating direction method of multipliers (ADMM), respectively.”
Learning to Optimize: A Primer and A Benchmark (2021, unknown): “Most of unrolling works following [75] also solve LASSO-type optimization problems for sparse coding [190; 161; 171; 72; 262; 4; 78; 59; 232].”
New Era for Robust Speech Recognition (2017, methodology): “It can be applied to distributed microphone arrays [22], but this chapter describes three similar systems for performing unsupervised spatial clustering and beamforming with compact microphone arrays [29, 37, 49].”