Add to ORKG24 pages, including 2 pages of references and 10 pages of appendixIn machine learning, it is common to optimize the parameters of a probabilistic model, modulated by a somewhat ad hoc regularization term that penalizes some values of the parameters. Regularization terms appear naturally in Variational Inference (VI), a tractable way to approximate Bayesian posteriors: the loss to optimize contains a Kullback--Leibler divergence term between the approximate posterior and a Bayesian prior. We fully characterize which regularizers can arise this way, and provide a systematic way to compute the corresponding prior. This viewpoint also provides a prediction for useful values of the regularization factor in neural networks. We apply this framewor...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Supervised machine learning techniques have been very successful for a variety of tasks and domains ...
Abstract paper shows that the ave rage or most likely (optima l) esti Many of the processing tasks a...
24 pages, including 2 pages of references and 10 pages of appendixIn machine learning, it is common ...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived pri...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
. In order to avoid overfitting in neural learning, a regularization term is added to the loss funct...
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived pri...
International audienceWe investigate deep Bayesian neural networks with Gaussian priors on the weigh...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
In linear regression problems with many predictors, penalized regression techniques are often used t...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training ...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Supervised machine learning techniques have been very successful for a variety of tasks and domains ...
Abstract paper shows that the ave rage or most likely (optima l) esti Many of the processing tasks a...
24 pages, including 2 pages of references and 10 pages of appendixIn machine learning, it is common ...
Deep neural networks have bested notable benchmarks across computer vision, reinforcement learning, ...
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived pri...
Neural Networks are famous for their advantageous flexibility for problems when there is insufficie...
. In order to avoid overfitting in neural learning, a regularization term is added to the loss funct...
Existing Bayesian models, especially nonparametric Bayesian methods, rely on specially conceived pri...
International audienceWe investigate deep Bayesian neural networks with Gaussian priors on the weigh...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
Dropout, a stochastic regularisation technique for training of neural networks, has recently been re...
In linear regression problems with many predictors, penalized regression techniques are often used t...
Variational inference with a factorized Gaussian posterior estimate is a widely-used approach for l...
Gaussian multiplicative noise is commonly used as a stochastic regularisation technique in training ...
Recent studies have shown that the generalization ability of deep neural networks (DNNs) is closely ...
Supervised machine learning techniques have been very successful for a variety of tasks and domains ...
Abstract paper shows that the ave rage or most likely (optima l) esti Many of the processing tasks a...