Stochastic Bayesian Neural Networks
Bayesian neural networks perform variational inference over the weights however calculation of the posterior distribution remains a challenge. Our work builds on variational inference techniques for bayesian neural networks using the original Evidence Lower Bound. In this paper, we present a stochastic bayesian neural network in which we maximize Evidence Lower Bound using a new objective function which we name as Stochastic Evidence Lower Bound. We evaluate our network on 5 publicly available UCI datasets using test RMSE and log likelihood as the evaluation metrics. We demonstrate that our work not only beats the previous state of the art algorithms but is also scalable to larger datasets.
Code (0)
등록된 구현이 없습니다.
Tasks
Variational InferenceSimilar Papers 제목 키워드 기반
Bayesian posterior approximation with stochastic ensembles
We introduce ensembles of stochastic neural networks to approximate the Bayesian posterior, combining stochastic methods such as dropout with deep ensembles. The stochastic ensembles are formulated as families of distrib…
Bayesian Inferenceimage-classificationImage ClassificationVariational InferencePAC-Bayesian-Like Error Bound for a Class of Linear Time-Invariant Stochastic State-Space Models
In this paper we derive a PAC-Bayesian-Like error bound for a class of stochastic dynamical systems with inputs, namely, for linear time-invariant stochastic state-space models (stochastic LTI systems for short). This cl…
EconometricsState Space ModelsLarge Bayesian Tensor VARs with Stochastic Volatility
We consider Bayesian tensor vector autoregressions (TVARs) in which the VAR coefficients are arranged as a three-dimensional array or tensor, and this coefficient tensor is parameterized using a low-rank CP decomposition…
Evaluating the Robustness of Bayesian Neural Networks Against Different Types of Attacks
To evaluate the robustness gain of Bayesian neural networks on image classification tasks, we perform input perturbations, and adversarial attacks to the state-of-the-art Bayesian neural networks, with a benchmark CNN mo…
Decision Makingimage-classificationImage ClassificationStochastic Control Barrier Functions with Bayesian Inference for Unknown Stochastic Differential Equations
Control barrier functions are widely used to synthesize safety-critical controls. However, the presence of Gaussian-type noise in dynamical systems can generate unbounded signals and potentially result in severe conseque…
Bayesian Inference