https://github.com/pytorch/botorch
공식 코드Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization
Papers with Code 아카이브
Second-order methods
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Use second-order statistics to process data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/renyiryry/tnt_neurips_2021
공식 코드https://github.com/rmclarke/adamthroughasecondorderlens
공식 코드Studying K-FAC Heuristics by Viewing Adam through a Second-Order Lens
https://github.com/rose-stl-lab/symmetry-teleportation
공식 코드https://github.com/senna1128/inf-stosqp
공식 코드https://github.com/svrnewton/svrn
공식 코드Stochastic Variance-Reduced Newton: Accelerating Finite-Sum Minimization with Large Batches
https://github.com/tomoleary/MatrixFreeNewton.jl
공식 코드Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization
https://github.com/tomoleary/hessianlearn
공식 코드Low Rank Saddle Free Newton: A Scalable Method for Stochastic Nonconvex Optimization
https://github.com/wangronin/hypervolumederivatives
공식 코드https://github.com/weikuang97/SketchedNT-Inf
공식 코드Online Covariance Matrix Estimation in Sketched Newton Methods
https://github.com/yorkerlin/remove-the-square-root
공식 코드Can We Remove the Square-Root in Adaptive Gradient Methods? A Second-Order Perspective
https://github.com/yuqiawu/hpgsrn
공식 코드https://github.com/GCaptainNemo/optimization-project
Optimization Methods for Supervised Machine Learning: From Linear Models to Deep Learning
https://github.com/Johanpdrsn/Newtonian-Monte-Carlo
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
https://github.com/aai-institute/pyDVL
Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/alstonlo/torch-influence
Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/cor3bit/somax
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
https://github.com/darkonhub/darkon
Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/davda54/ada-hessian
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
https://github.com/emaballarin/nmc-numpyro
Newtonian Monte Carlo: single-site MCMC meets second-order gradient methods
https://github.com/imoneoi/openchat
https://github.com/joaompereira/hipnex
Generalized Optimistic Methods for Convex-Concave Saddle Point Problems
https://github.com/morganmcg1/ImageNette_ImageWoof_ImageWang
ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
https://github.com/nathansiae/Stochastic-Average-Newton
Stochastic Newton and Cubic Newton Methods with Simple Local Linear-Quadratic Rates
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.