Search Results for author: Boris Flach

Found 10 papers, 0 papers with code

Symmetric Equilibrium Learning of VAEs

no code implementations19 Jul 2023 Boris Flach, Dmitrij Schlesinger, Alexander Shekhovtsov

We propose a Nash equilibrium learning approach that relaxes these restrictions and allows learning VAEs in situations where both the data and the latent distributions are accessible only by sampling.

VAE Approximation Error: ELBO and Exponential Families

no code implementations ICLR 2022 Alexander Shekhovtsov, Dmitrij Schlesinger, Boris Flach

The importance of Variational Autoencoders reaches far beyond standalone generative models -- the approach is also used for learning latent representations and can be generalized to semi-supervised learning.

Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks

no code implementations NeurIPS 2020 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.

Feed-forward Propagation in Probabilistic Neural Networks with Categorical and Max Layers

no code implementations ICLR 2019 Alexander Shekhovtsov, Boris Flach

Probabilistic Neural Networks deal with various sources of stochasticity: input noise, dropout, stochastic neurons, parameter uncertainties modeled as random variables, etc.

Test

Stochastic Normalizations as Bayesian Learning

no code implementations1 Nov 2018 Alexander Shekhovtsov, Boris Flach

In this work we investigate the reasons why Batch Normalization (BN) improves the generalization performance of deep networks.

Test

Normalization of Neural Networks using Analytic Variance Propagation

no code implementations28 Mar 2018 Alexander Shekhovtsov, Boris Flach

We address the problem of estimating statistics of hidden units in a neural network using a method of analytic moment propagation.

Feed-forward Uncertainty Propagation in Belief and Neural Networks

no code implementations28 Mar 2018 Alexander Shekhovtsov, Boris Flach, Michal Busta

We propose a feed-forward inference method applicable to belief and neural networks.

Generative learning for deep networks

no code implementations25 Sep 2017 Boris Flach, Alexander Shekhovtsov, Ondrej Fikar

Learning, taking into account full distribution of the data, referred to as generative, is not feasible with deep neural networks (DNNs) because they model only the conditional distribution of the outputs given the inputs.

Bayesian Inference

M-best solutions for a class of fuzzy constraint satisfaction problems

no code implementations23 Jul 2014 Michail Schlesinger, Boris Flach, Evgeniy Vodolazskiy

The article studies the problem of finding d most admissible solutions for a given d. A tractable subclass of these problems is defined by the concepts of invariants and polymorphisms similar to the classic constraint satisfaction approach.

A class of random fields on complete graphs with tractable partition function

no code implementations10 Dec 2012 Boris Flach

The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time.

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