10 papers
Evaluating Sparse Autoencoders: From Shallow Design to Matching Pursuit
Valérie Costa, Thomas Fel, Ekdeep Singh Lubana +2
Sparse autoencoders (SAEs) have recently become central tools for interpretability, leveraging dictionary learning principles to extract sparse, interpretable features from neural…
From Flat to Hierarchical: Extracting Sparse Representations with Matching Pursuit
Valérie Costa, Thomas Fel, Ekdeep Singh Lubana +2
Motivated by the hypothesis that neural network representations encode abstract, interpretable features as linearly accessible, approximately orthogonal directions, sparse autoenco…
A Training Framework for Stereo-Aware Speech Enhancement using Deep Neural Networks
Bahareh Tolooshams, Kazuhito Koishida
Deep learning-based speech enhancement has shown unprecedented performance in recent years. The most popular mono speech enhancement frameworks are end-to-end networks mapping the…
Unfolding Neural Networks for Compressive Multichannel Blind Deconvolution
Bahareh Tolooshams, Satish Mulleti, Demba Ba +1
We propose a learned-structured unfolding neural network for the problem of compressive sparse multichannel blind-deconvolution. In this problem, each channel's measurements are gi…
Channel-Attention Dense U-Net for Multichannel Speech Enhancement
Bahareh Tolooshams, Ritwik Giri, Andrew H. Song +2
Supervised deep learning has gained significant attention for speech enhancement recently. The state-of-the-art deep learning methods perform the task by learning a ratio/binary ma…
RandNet: deep learning with compressed measurements of images
Thomas Chang, Bahareh Tolooshams, Demba Ba
Principal component analysis, dictionary learning, and auto-encoders are all unsupervised methods for learning representations from a large amount of training data. In all these me…