Search Results for author:
Found 10 papers, 1 papers with code
Symmetric Equilibrium Learning of VAEs
The flexibility and simplicity of this approach allows its application to a wide range of learning scenarios and downstream tasks.
VAE Approximation Error: ELBO and Exponential Families
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
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
Probabilistic Neural Networks deal with various sources of stochasticity: input noise, dropout, stochastic neurons, parameter uncertainties modeled as random variables, etc.
Stochastic Normalizations as Bayesian Learning
In this work we investigate the reasons why Batch Normalization (BN) improves the generalization performance of deep networks.
Normalization of Neural Networks using Analytic Variance Propagation
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
We propose a feed-forward inference method applicable to belief and neural networks.
Generative learning for deep networks
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.
M-best solutions for a class of fuzzy constraint satisfaction problems
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
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.
