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Found 5 papers, 2 papers with code
Maximizing the Success Probability of Policy Allocations in Online Systems
The effectiveness of advertising in e-commerce largely depends on the ability of merchants to bid on and win impressions for their targeted users.
A Primer on Bayesian Neural Networks: Review and Debates
Neural networks have achieved remarkable performance across various problem domains, but their widespread applicability is hindered by inherent limitations such as overconfidence in predictions, lack of interpretability, and vulnerability to adversarial attacks.
Dependence between Bayesian neural network units
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years, with the flagship result that hidden units converge to a Gaussian process limit when the layers width tends to infinity.
Bayesian neural network unit priors and generalized Weibull-tail property
The connection between Bayesian neural networks and Gaussian processes gained a lot of attention in the last few years.
Understanding Priors in Bayesian Neural Networks at the Unit Level
We investigate deep Bayesian neural networks with Gaussian weight priors and a class of ReLU-like nonlinearities.
