bayesian neural network

Noise Contrastive Priors for Functional Uncertainty UAI 2019 ODE2VAE: Deep generative second order ODEs with Bayesian neural networks NeurIPS 2019 Randomized Value Functions via Multiplicative Normalizing Flows UAI 2019 Robust Bayesian and Light Neural Networks for Voice Spoofing Detection Interspeech 2019 Sampling-Free Variational Inference of Bayesian Neural Networks by Variance Backpropagation UAI 2019 Statistical Guarantees for the Robustness of Bayesian Neural Networks IJCAI 2019 Understanding Priors in Bayesian Neural Networks at the Unit Level ICML 2019 Variance Networks: When Expectation Does Not Meet Your Expectations ICLR 2019 Variational Implicit Processes ICML 2019 Adversarial Distillation of Bayesian Neural Network Posteriors ICML 2018 Assessing Quality Estimation Models for Sentence-Level Prediction COLING 2018 Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning ICML 2018 Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson Sampling ICLR 2018 Flipout: Efficient Pseudo-Independent Weight Perturbations on Mini-Batches ICLR 2018 Kernel Implicit Variational Inference ICLR 2018 Learning Structural Weight Uncertainty for Sequential Decision-Making AISTATS 2018 Noisy Natural Gradient as Variational Inference ICML 2018 Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors ICML 2018 Variational Bayesian dropout: pitfalls and fixes ICML 2018 Variational Inference with Tail-adaptive f-Divergence NeurIPS 2018 Dropout Inference in Bayesian Neural Networks with Alpha-divergences ICML 2017 Learning Structured Weight Uncertainty in Bayesian Neural Networks AISTATS 2017 Multiplicative Normalizing Flows for Variational Bayesian Neural Networks ICML 2017 Reducing Reparameterization Gradient Variance NeurIPS 2017 Robust and Efficient Transfer Learning with Hidden Parameter Markov Decision Processes NeurIPS 2017