77 citations · 169 across the 26 of their papers we have counts for
16 papers · 1 filter
Implicit Sparse Regularization: The Impact of Depth and Early Stopping
Jiangyuan Li, Thanh V. Nguyen, Chinmay Hegde +1
In this paper, we study the implicit bias of gradient descent for sparse regression. We extend results on regression with quadratic parametrization, which amounts to depth-2 diagon…
Provable Compressed Sensing with Generative Priors via Langevin Dynamics
Thanh V. Nguyen, Gauri Jagatap, Chinmay Hegde
Deep generative models have emerged as a powerful class of priors for signals in various inverse problems such as compressed sensing, phase retrieval and super-resolution. Here, we…
Signal Reconstruction from Modulo Observations
Viraj Shah, Chinmay Hegde
We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known…
Autoencoders Learn Generative Linear Models
Thanh V. Nguyen, Raymond K. W. Wong, Chinmay Hegde
We provide a series of results for unsupervised learning with autoencoders. Specifically, we study shallow two-layer autoencoder architectures with shared weights. We focus on thre…
On Consensus-Optimality Trade-offs in Collaborative Deep Learning
Zhanhong Jiang, Aditya Balu, Chinmay Hegde +1
In distributed machine learning, where agents collaboratively learn from diverse private data sets, there is a fundamental tension between consensus and optimality. In this paper,…
On Learning Sparsely Used Dictionaries from Incomplete Samples
Thanh V. Nguyen, Akshay Soni, Chinmay Hegde
Most existing algorithms for dictionary learning assume that all entries of the (high-dimensional) input data are fully observed. However, in several practical applications (such a…