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Bayesian Inference

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Papers

On Contrastive Learning for Likelihood-free Inference ICML 2020
Efficient and Scalable Bayesian Neural Nets with Rank-1 Factors ICML 2020
Sparse Gaussian Processes with Spherical Harmonic Features ICML 2020
Accelerating the diffusion-based ensemble sampling by non-reversible dynamics ICML 2020
Task-Oriented Active Perception and Planning in Environments with Partially Known Semantics ICML 2020
Automatic Reparameterisation of Probabilistic Programs ICML 2020
Recurrent Hierarchical Topic-Guided RNN for Language Generation ICML 2020
Likelihood-free MCMC with Amortized Approximate Ratio Estimators ICML 2020
Optimizing Dynamic Structures with Bayesian Generative Search ICML 2020
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
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