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Bayesian Inference

5,566 papers

Papers per year

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100
176
522
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Papers

Robust, Accurate Stochastic Optimization for Variational Inference NIPS 2020
Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations NIPS 2020
Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling NIPS 2020
Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes NIPS 2020
Incorporating Interpretable Output Constraints in Bayesian Neural Networks NIPS 2020
ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA NIPS 2020
Uncertainty Aware Semi-Supervised Learning on Graph Data NIPS 2020
VarGrad: A Low-Variance Gradient Estimator for Variational Inference NIPS 2020
Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition NIPS 2020
Point process models for sequence detection in high-dimensional neural spike trains NIPS 2020
Meta-Consolidation for Continual Learning NIPS 2020
Spike and slab variational Bayes for high dimensional logistic regression NIPS 2020
A Variational Approach for Learning from Positive and Unlabeled Data NIPS 2020
Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations NIPS 2020
Deep Evidential Regression NIPS 2020
Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks NIPS 2020
Bayesian Pseudocoresets NIPS 2020
Deep Rao-Blackwellised Particle Filters for Time Series Forecasting NIPS 2020
BOSS: Bayesian Optimization over String Spaces NIPS 2020
Markovian Score Climbing: Variational Inference with KL(p||q) NIPS 2020
On the Expressiveness of Approximate Inference in Bayesian Neural Networks NIPS 2020
Decentralized Langevin Dynamics for Bayesian Learning NIPS 2020
Variational Bayesian Unlearning NIPS 2020
Certifiably Adversarially Robust Detection of Out-of-Distribution Data NIPS 2020
Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels NIPS 2020