Bayesian Inference
1개 벤치마크 · 논문 2,612편 · 이 태스크의 논문 보기 →
Benchmarks
cifar100
Most implemented
Weight Uncertainty in Neural Networks
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
Variational Autoencoders for Collaborative Filtering
Semi-Supervised Learning with Deep Generative Models
Stein Variational Gradient Descent: A General Purpose Bayesian Inference Algorithm
Bayesian regression and Bitcoin
Papers
On the approximation of posterior laws in compound loss models by conditional Wasserstein GANs
Bayesian inference in compound loss models must often be repeated across policies, market scenarios, and prior specifications. Outside conjugate cases, this may require repeated numerical integration or Markov chain Mont…
Bayesian InferenceSpike-based Belief Propagation in Nonlinear Dynamical Systems
This paper presents a Bayesian control framework that integrates spike-based dynamics with probabilistic inference for adaptive control. Bayesian inference is widely regarded as a core computational principle of brain fu…
Bayesian InferenceScalable Amortized Variational Inference for Non-Poisson Buy-'Til-You-Die Models
Despite the wide variety of existing Buy-`Til-You-Die (BTYD) models, nearly all rely upon the convenient assumption of transactions following a Poisson process. As modern customer bases grow larger and more diverse, a ma…
Bayesian InferenceHeteroscedastic Neural Surrogate Modeling for Robust and Rapid Bayesian Inference in Fusion Plasma Diagnostics
Bayesian inference via Markov Chain Monte Carlo (MCMC) provides effective parameter estimation, but its real-time application in complex physical systems is hindered by heavy computational bottlenecks and extreme sensiti…
Bayesian InferenceKnowing When to Stop: Bayesian Optimal Stopping for LLM Evaluations
LLM evaluations often use fixed sampling budgets, testing every item the same number of times even after estimates are precise. We introduce optstop, a precision-based adaptive stopping framework that treats evaluation a…
Bayesian InferenceA Hybrid Nested Harness for Decoupling Structure and Parameters in LLM-Driven Optimization
In evolutionary algorithms powered by language models, the LLM acts as a single operator that simultaneously updates structural components (like control flow) and continuous parameters. While LLMs can be good at the firs…
Bayesian Inference