Bayesian Neural Network Recent Papers
a repo sharing Bayesian Neural Network recent papers
Methods
Variational Inference (VI)
[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
[17] Mixed Variational Inference, arXiv 2019
[18] Radial and Directional Posteriors for Bayesian Neural Networks, arXiv 2019
[19] Unbiased Implicit Variational Inference, arXiv 2019
[20] Semi-implicit variational inference, ICML 2018
[21] Doubly Semi-Implicit Variational Inference, arXiv 2018
[22] Automated Variational Inference in Probabilistic Programming, arXiv 2013
[23] Black Box Variational Inference, arXiv 2014
[24] Stochastic Variational Inference, JMLR 2013
[25] Weight Uncertainty in Neural Networks, ICML 2015
[26] Functional Variational Bayesian Neural Networks
Markov Chain Monte Carlo
[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
[7] Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning, arXiv 2019
[8] Communication-Efficient Stochastic Gradient MCMC for Neural Networks, AAAI 2019
[9] Stochastic Gradient MCMC with Stale Gradients, NIPS 2016
[10] Markov Chain Monte Carlo and Variational Inference: Bridging the Gap, ICML 2015
[11] CPSG-MCMC: Clustering-Based Preprocessing method for Stochastic Gradient MCMC, AISTATS 2017
MCMC + VI
[1] Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization, AISTATS 2016
[2] Auxiliary Variational MCMC, ICLR 2019
[3] Variational MCMC, UAI 2011
[4] Variational Hamiltonian Monte Carlo via Score Matching, 2018
Ensembling Sampling (ES)
[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
Particle Optimization
[1] Function Space Particle Optimization for Bayesian Neural Networks, ICLR 2019
[2] A Unified Particle-Optimization Framework for Scalable Bayesian Sampling, UAI 2018
[3] Bayesian posterior approximation via greedy particle optimization, arXiv 2019
Laplace Approximation
[1] Online Structured Laplace Approximations For Overcoming Catastrophic Forgetting, arXiv 2018
[2] A Scalable Laplace Approximation for Neural Networks, ICLR 2018
Expectation Propgation (EP)
[1] Assumed Density Filtering Methods for Learning Bayesian Neural Networks, AAAi 2016
[2] Probabilistic Backpropagation for Scalable Learning of Bayesian Neural Networks, ICML 2016
Others
[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
Theory
Gaussian Process
[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
Dropout
[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
Issues
[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
Others
[1] Statistical Guarantees for the Robustness of Bayesian Neural Networks, arXiv 2019
[2] Performance Measurement for Deep Bayesian Neural Network
Applications
Adversarial Defense
[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
[4] Predictive Uncertainty Quantification with Compound Density Networks, arXiv 2019
Bayesian Optmization
[1] Learning Curve Prediction with Bayesian Neural Networks, ICLR 2017
[2] Bayesian Optimization with Robust Bayesian Neural Networks, NIPS 2016
Hardware Acceleration
[1] VIBNN Hardware Acceleration of Bayesian Neural Networks, ASPLOS 2018
Regression
[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
Implicit Multivariate Prior
[1] Variational Implicit Processes, NIPS 2018
Classification
[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
Reinforcement Learning
[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
[5] Uncertainty Aware Learning from Demonstrations in Multiple Contexts using Bayesian Neural Networks
[6] Stein Variational Policy Gradient, UAI 2018
[7] Variational Inference for Policy Gradient, arXiv 2018
Recurrent/Convolutional Neural Networks
[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
[8] Learning Weight Uncertainty with SG-MCMC for Shape Classification, CVPR 2016
Incremental Learning
[1] BAYESIAN INCREMENTAL LEARNING FOR DEEP NEURAL NETWORKS, ICLR 2018
GAN
[1] Bayesian GAN, NIPS 2017
ODE
[1] ODE2VAE: Deep generative second order ODEs with Bayesian neural networks, arXiv 2019
Interpretable AI
[1] Interpretable Outcome Prediction with Sparse Bayesian Neural Networks in Intensive Care, arXiv 2019
Computational Fluid Dynamics
[1] Quantifying model form uncertainty in Reynolds-averaged turbulence models with Bayesian deep neural networks, arXiv 2019
Survey
[1] Towards Bayesian Deep Learning A Survey, arXiv 2016
[2] Advances in Variational Inference, arXiv 2017
[3] Variational Inference: A Review for Statisticians, arXiv 2018
Thesis
[1] UNCERTAINTY IN DEEP LEARNING, Yarin Gal 2016
[2] Approximate Inference: New Visions, Yingzhen Li 2018
[3] Towards Better Representations with Deep/Bayes Learning, Chunyuan Li 2018 (slide)
[4] Variational Inference & Deep Learning: A new Synthesis, D.P. Kingma 2017
[5] Bayesian Convolutional Neural Network, Shridhar Kumar 2018
[6] BAYESIAN LEARNING FOR NEURAL NETWORKS, Radford M. Neal 1995
[7] Stochastic Gradient MCMC: Algorithms and Applications, Sungjin Ahn 2015
[8] Large-Scale Bayesian Computation Using Stochastic Gradient Markov Chain Monte Carlo, Jack Baker 2018
Researcher
[1] Max Welling
[3] Radford M. Neal
[4] Yee Whye Teh
[5] Ryan P. Adams
[6] David M. Blei
[7] Jun Zhu
[8] Lawrence Carin
[10] Roger Grosse
[11] Yarin Gal
[12] Yingzhen Li
[13] Yutian Chen
[14] Christos Louizos
[15] Charles Blundell
[16] Shengyang sun
[17] Jiaxin Shi
[18] Chunyuan Li
[19] Mingyuan Zhou
[20] Sungjin Ahn
[21] Yian Ma
[22] Emily Fox
[23] Qiang Liu
[24] eric nalisnick
Events
[1] NIPS Bayesian Deep Learning Workshop (2016, 2017, 2018)
[2] NIPS Symposium on Advances in Approximate Bayesian Inference (2014 - 2018)