11 citations · 13 across the 3 of their papers we have counts for
3 papers
Compression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
Kevin Kögler, Alexander Shevchenko, Hamed Hassani +1
Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shall…
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
Alexander Shevchenko, Kevin Kögler, Hamed Hassani +1
Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the fea…
Estimation in Rotationally Invariant Generalized Linear Models via Approximate Message Passing
Ramji Venkataramanan, Kevin Kögler, Marco Mondelli
We consider the problem of signal estimation in generalized linear models defined via rotationally invariant design matrices. Since these matrices can have an arbitrary spectral di…