3 citations · 3 across the 3 of their papers we have counts for
3 papers
stat.ML2020
Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
Alexander Shekhovtsov, Viktor Yanush
Training neural networks with binary weights and activations is a challenging problem due to the lack of gradients and difficulty of optimization over discrete weights. Many succes…
stat.ML2020★ 3 cited
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
Alexander Shekhovtsov, Viktor Yanush, Boris Flach
In neural networks with binary activations and or binary weights the training by gradient descent is complicated as the model has piecewise constant response. We consider stochasti…
stat.ML2019
Hamiltonian Monte-Carlo for Orthogonal Matrices
Viktor Yanush, Dmitry Kropotov
We consider the problem of sampling from posterior distributions for Bayesian models where some parameters are restricted to be orthogonal matrices. Such matrices are sometimes use…