a repo sharing Bayesian Neural Network recent papers
[1] Variational Bayesian Phylogenetic Inference , ICLR 2019
[2] FUNCTIONAL VARIATIONAL BAYESIAN NEURAL NETWORKS, ICLR 2019
[3] Deterministic Variational Inference for Robust Bayesian Neural Networks , ICLR 2019
[4] Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors, ICML 2018
[5] Projected BNNs: Avoiding weight-space pathologies by learning latent representations of neural network weights, arXiv 2018
[6] Noisy Natural Gradient as Variational Inference, ICML 2018
[7] Neural Control Variates for Variance Reduction, arXiv 2018
[8] Message Passing Stein Variational Gradient Descent, ICML 2018
[9] KERNEL IMPLICIT VARIATIONAL INFERENCE, ICLR 2018
[10] Gradient Estimators for Implicit Models, ICLR 2018
[11] Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks, arXiv 2018
[12] Reducing Reparameterization Gradient Variance, NIPS 2017
[13] Multiplicative Normalizing Flows for Variational Bayesian Neural Networks, ICML 2017
[14] Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm, NIPS 2016
[15] Known Unknowns: Uncertainty Quality in Bayesian Neural Networks, NIPS 2016
[16] Practical Variational Inference for Neural Networks, NIPS 2011
[1] Meta-Learning For Stochastic Gradient MCMC, ICLR 2019
[2] Adversarial Distillation of Bayesian Neural Network Posteriors, ICML 2018
[3] Preconditioned Stochastic Gradient Langevin Dynamics for Deep Neural Networks, AAAI 2016
[4] Bayesian Dark Knowledge, NIPS 2015
[5] Stochastic Gradient Hamiltonian Monte Carlo, ICML 2014
[6] Bayesian learning via stochastic gradient langevin dynamics, ICML 2011
[1] Uncertainty in Neural Networks: Bayesian Ensembling, arXiv 2019
[2] A Simple Baseline for Bayesian Uncertainty in Deep Learning, arXiv 2019
[3] Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam, ICML 2018
[4] Bayesian Neural Network Ensembles, NIPS 2018
[5] Averaging Weights Leads to Wider Optima and Better Generalization, UAI 2018
[6]Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles, NIPS 2017
[1] Function Space Particle Optimization for Bayesian Neural Networks, ICLR 2019
[2] A Unified Particle-Optimization Framework for Scalable Bayesian Sampling, UAI 2018
[1] Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting, arXiv 2018
[2] A Scalable Laplace Approximation for Neural Networks, ICLR 2018
[1] Assumed Density Filtering Methods for Learning Bayesian Neural Networks, AAAi 2016
[2] Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, ICML 2016
[1] Practical Bayesian Learning of Neural Networks via Adaptive Subgradient Methods, Under Review AISTATS 2019
[2] Learning Structured Weight Uncertainty in Bayesian Neural Networks, AISTATS 2017
[1] Bayesian Deep Convolutional Networks with Many Channels are Gaussian Processes , ICLR 2019
[2] Closed Form Variational Objectives For Bayesian Neural Networks with a Single Hidden Layer, NIPS 2018
[3] Building Bayesian Neural Networks with Blocks: On Structure, Interpretability and Uncertainty, arXiv 2018
[4] Mapping Gaussian Process Priors to Bayesian Neural Networks, NIPS 2017
[1] Variational Bayesian dropout: pitfalls and fixes, ICML 2018
[2] Loss-Calibrated Approximate Inference in Bayesian Neural Networks, arXiv 2018
[3] Variational Dropout Sparsifies Deep Neural Networks, ICML 2017
[4] Dropout Inference in Bayesian Neural Networks with Alpha-divergences, ICML 2017
[5] Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning, ICML 2016
[1] Overpruning in Variational Bayesian Neural Networks, NIPS 2017
[2] Bayesian neural networks increasingly sparsify their units with depth, arXiv 2018
[3] Accelerated First-order Methods on the Wasserstein Space for Bayesian Inference, arXiv 2018
[1] Understanding Measures of Uncertainty for Adversarial Example Detection, UAI 2018
[2] Sufficient Conditions for Robustness to Adversarial Examples: a Theoretical and Empirical Study with Bayesian Neural Networks, ICLR 2019
[3] Adv-BNN: Improved Adversarial Defense through Robust Bayesian Neural Network, ICLR 2019
[1] Learning Curve Prediction with Bayesian Neural Networks, ICLR 2017
[2] Bayesian Optimization with Robust Bayesian Neural Networks, NIPS 2016
[1] VIBNN Hardware Acceleration of Bayesian Neural Networks, ASPLOS 2018
[1] Applying SVGD to Bayesian Neural Networks for Cyclical Time-Series Prediction and Inference, NIPS 2018
[2] Informed MCMC with Bayesian Neural Networks for Facial Image Analysis, NIPS 2018
[3] Accurate Uncertainties for Deep Learning Using Calibrated Regression, ICML 2018
[4] Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks, CVPR 2017
[1] Variational Implicit Processes, NIPS 2018
[1] Uncertainty quantification using Bayesian neural networks in classification: Application to ischemic stroke lesion segmentation, MIDL 2018
[2] Hierarchical Bayesian Neural Networks for Personalized Classification, NIPS 2016
[1] Randomized Prior Functions for Deep Reinforcement Learning, NIPS 2018
[2] Learning Structural Weight Uncertainty for Sequential Decision-Making, AISTATS 2018
[3] VIME: Variational Information Maximizing Exploration, arXiv 2017
[4] Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks, ICLR 2017
[1] Bayesian Recurrent Neural Networks, arXiv 2018
[2] Bayesian Convolutional Neural Networks with Variational Inference, arXiv 2018
[3] Scalable Bayesian Learning of Recurrent Neural Networks for Language Modeling, ACL 2017
[4] Propagating Uncertainty in Multi-Stage Bayesian Convolutional Neural Networks with Application to Pulmonary Nodule Detection, NIPS 2017
[5] Bayesian Sparsification of Recurrent Neural Networks, arXiv 2017
[6] BAYESIAN CONVOLUTIONAL NEURAL NETWORKS WITH BERNOULLI APPROXIMATE VARIATIONAL INFERENCE, ICLR 2016
[7] Sparse Bayesian Recurrent Neural Networks, ECML PKDD 2015
[1] BAYESIAN INCREMENTAL LEARNING FOR DEEP NEURAL NETWORKS, ICLR 2018
[1] Bayesian GAN, NIPS 2017
[1] Towards Bayesian Deep Learning A Survey, arXiv 2016
[2] Advances in Variational Inference, arXiv 2017
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