arXiv:2307.04675
공식 코드 · Papers with Code
LINFA: a Python library for variational inference with normalizing flow and annealing
원문 초록
Variational inference is an increasingly popular method in statistics and machine learning for approximating probability distributions. We developed LINFA (Library for Inference with Normalizing Flow and Annealing), a Python library for variational inference to accommodate computationally expensive models and difficult-to-sample distributions with dependent parameters. We discuss the theoretical background, capabilities, and performance of LINFA in various benchmarks. LINFA is publicly available on GitHub at https://github.com/desResLab/LINFA.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Interpretable Bayesian Tensor Network Kernel Machines with Automatic Rank and Feature Selection
Scalable Bayesian Low-Rank Adaptation of Large Language Models via Stochastic Variational Subspace Inference
Variational Inference with Mixtures of Isotropic Gaussians
Amortized variational transdimensional inference
Synonymous Variational Inference for Perceptual Image Compression
Bayesian Meta-Reinforcement Learning with Laplace Variational Recurrent Networks
토론
이 논문과 연결된 포럼 글이 아직 없습니다.