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Reintroducing Straight-Through Estimators as Principled Methods for Stochastic Binary Networks
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.
Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks
In neural networks with binary activations and or binary weights the training by gradient descent is complicated as the model has piecewise constant response.
Hamiltonian Monte-Carlo for Orthogonal Matrices
In \citet{byrne2013geodesic} authors have already considered sampling from distributions over manifolds using exact geodesic flows in a scheme similar to Hamiltonian Monte Carlo (HMC).
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