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Bayesian Inference

Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).

Papers

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Showing 1251–1300 of 2226 papers

TitleStatusHype
Structured Dropout Variational Inference for Bayesian Neural Networks—0
ScrofaZero: Mastering Trick-taking Poker Game Gongzhu by Deep Reinforcement LearningCode0
Neural Posterior Regularization for Likelihood-Free InferenceCode0
Projected Wasserstein gradient descent for high-dimensional Bayesian inferenceCode0
Bayesian Inference with Certifiable Adversarial RobustnessCode0
Bayesian multiscale deep generative model for the solution of high-dimensional inverse problemsCode0
Bayesian Neural Networks for Virtual Flow Metering: An Empirical StudyCode0
Bayesian data-driven discovery of partial differential equations with variable coefficients—0
Modeling German Word Order Acquisition via Bayesian Inference—0
Fundamental limits and algorithms for sparse linear regression with sublinear sparsity—0
Human Inference in Changing Environments With Temporal Structure—0
Bayesian Inference ForgettingCode0
Probabilistic Inference for Learning from Untrusted Sources—0
On the relationship between a Gamma distributed precision parameter and the associated standard deviation in the context of Bayesian parameter inferenceCode0
Scaling Up Bayesian Uncertainty Quantification for Inverse Problems using Deep Neural Networks—0
Maximum a Posteriori Inference of Random Dot Product Graphs via Conic Programming—0
Control-Data Separation and Logical Condition Propagation for Efficient Inference on Probabilistic Programs—0
Learning optimal Bayesian prior probabilities from data—0
Uncertainty Calibration Error: A New Metric for Multi-Class Classification—0
Bayesian Neural Networks with Variance Propagation for Uncertainty Evaluation—0
Scalable Bayesian Inverse Reinforcement Learning by Auto-Encoding Reward—0
A Bayesian-Symbolic Approach to Learning and Reasoning for Intuitive Physics—0
Bayesian Context Aggregation for Neural Processes—0
Variational Multi-Task Learning—0
A Tutorial on Sparse Gaussian Processes and Variational Inference—0
On Batch Normalisation for Approximate Bayesian Inference—0
Gradient-Free Adversarial Attacks for Bayesian Neural NetworksCode0
Alternating linear scheme in a Bayesian framework for low-rank tensor approximation—0
Recent advances in deep learning theory—0
Forming Real-World Human-Robot Cooperation for Tasks With General Goal—0
Lévy walks derived from a Bayesian decision-making model in non-stationary environments—0
Bayesian neural network with pretrained protein embedding enhances prediction accuracy of drug-protein interactionCode0
Bayesian Neural Ordinary Differential Equations—0
Probabilistic Latent Factor Model for Collaborative Filtering with Bayesian InferenceCode0
Noise and Fluctuation of Finite Learning Rate Stochastic Gradient Descent—0
Semi-Supervised Learning with Variational Bayesian Inference and Maximum Uncertainty RegularizationCode0
Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks—0
Generalised Bayesian Filtering via Sequential Monte Carlo—0
Bidirectional Convolutional Poisson Gamma Dynamical SystemsCode0
Baxter Permutation ProcessCode0
Bayesian PseudocoresetsCode0
Online Bayesian Goal Inference for Boundedly Rational Planning Agents—0
Distributed Variational Bayesian Algorithms Over Sensor Networks—0
Free Energy Minimization: A Unified Framework for Modelling, Inference, Learning,and Optimization—0
Variational Refinement for Importance SamplingUsing the Forward Kullback-Leibler Divergence—0
On the Inconsistency of Bayesian Inference for Misspecified Neural Networks—0
Parametric Bootstrap Ensembles as Variational Inference—0
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models—0
Gaussian Density Parametrization Flow: Particle and Stochastic Approaches—0
Understanding Variational Inference in Function-SpaceCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1F-SWAAccuracy83.61—Unverified
2F-SWAGAccuracy80.93—Unverified