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Found 3 papers, 0 papers with code
Deep Network Regularization via Bayesian Inference of Synaptic Connectivity
To alleviate this burden, in this paper we regard the DNN regularization problem from a Bayesian inference perspective: We impose a sparsity-inducing prior over the network synaptic weights, where the sparsity is induced by a set of Bernoulli-distributed binary variables with Beta (hyper-)priors over their prior parameters.
A Nonparametric Bayesian Approach Toward Stacked Convolutional Independent Component Analysis
Unsupervised feature learning algorithms based on convolutional formulations of independent components analysis (ICA) have been demonstrated to yield state-of-the-art results in several action recognition benchmarks.
Indian Buffet Process Deep Generative Models for Semi-Supervised Classification
Based on this inspiration, in this paper we examine whether similar ideas from the field of Bayesian nonparametrics can be utilized in the context of modern DGMs in order to address the latent variable dimensionality inference problem.
