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Bayesian Inference

5,566 papers

Papers per year

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Papers

Black-Box Variational Inference as a Parametric Approximation to Langevin Dynamics ICML 2020
Infinite attention: NNGP and NTK for deep attention networks ICML 2020
From Importance Sampling to Doubly Robust Policy Gradient ICML 2020
Deep Graph Random Process for Relational-Thinking-Based Speech Recognition ICML 2020
Parametric Gaussian Process Regressors ICML 2020
Source Separation with Deep Generative Priors ICML 2020
BINOCULARS for efficient, nonmyopic sequential experimental design ICML 2020
Being Bayesian about Categorical Probability ICML 2020
Stochastic Differential Equations with Variational Wishart Diffusions ICML 2020
Efficient Non-conjugate Gaussian Process Factor Models for Spike Count Data using Polynomial Approximations ICML 2020
Differentiable Likelihoods for Fast Inversion of ’Likelihood-Free’ Dynamical Systems ICML 2020
Bayesian Experimental Design for Implicit Models by Mutual Information Neural Estimation ICML 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020
Estimating Model Uncertainty of Neural Networks in Sparse Information Form ICML 2020
AR-DAE: Towards Unbiased Neural Entropy Gradient Estimation ICML 2020
Handling the Positive-Definite Constraint in the Bayesian Learning Rule ICML 2020
Adversarial Neural Pruning with Latent Vulnerability Suppression ICML 2020
Stochastically Dominant Distributional Reinforcement Learning ICML 2020
On Approximate Thompson Sampling with Langevin Algorithms ICML 2020
Neural Datalog Through Time: Informed Temporal Modeling via Logical Specification ICML 2020
Training Binary Neural Networks using the Bayesian Learning Rule ICML 2020
Projective Preferential Bayesian Optimization ICML 2020
Bayesian Sparsification of Deep C-valued Networks ICML 2020
Involutive MCMC: a Unifying Framework ICML 2020
Knowing The What But Not The Where in Bayesian Optimization ICML 2020