https://github.com/lacava/feat
공식 코드Learning concise representations for regression by evolving networks of trees
Papers with Code 아카이브
Stochastic Optimization
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- 12
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- 35
Stochastic Optimization is the task of optimizing certain objective functional by generating and using stochastic random variables. Usually the Stochastic Optimization is an iterative process of generating random variables that progressively finds out the minima or the maxima of the objective functional. Stochastic Optimization is usually applied in the non-convex functional spaces where the usual deterministic optimization such as linear or quadratic programming or their variants cannot be used.
Source: ASOC: An Adaptive Parameter-free Stochastic Optimization Techinique for Continuous Variables
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/lacava/iclr_2019
공식 코드Learning concise representations for regression by evolving networks of trees
https://github.com/lakshmimandal/n_steptd_opt_n
공식 코드https://github.com/lancopku/AdaMod
공식 코드An Adaptive and Momental Bound Method for Stochastic Learning
https://github.com/lasgroup/lbsgd-rl
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/leepei/dplbfgs
공식 코드A Distributed Quasi-Newton Algorithm for Empirical Risk Minimization with Nonsmooth Regularization
https://github.com/leonyuhe/tlwalk
공식 코드https://github.com/liboyue/Network-Distributed-Algorithm
공식 코드https://github.com/liehe/prfl
공식 코드https://github.com/limaries30/distributed-td-learning
공식 코드https://github.com/lis-epfl/Tensoft-G21
공식 코드https://github.com/lisacoiffard/aurora-xcon
공식 코드Overcoming Deceptiveness in Fitness Optimization with Unsupervised Quality-Diversity
https://github.com/litian96/FedDANE
공식 코드https://github.com/litian96/FedProx
공식 코드https://github.com/locuslab/e2e-model-learning
공식 코드Task-based End-to-end Model Learning in Stochastic Optimization
https://github.com/lolemacs/avagrad
공식 코드https://github.com/loshchil/SGDR
공식 코드https://github.com/loujiabin1994/evolutionary_programmer
공식 코드https://github.com/lowya/Locally-Differentially-Private-Federated-Learning
공식 코드Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
https://github.com/lowya/private-federated-learning-without-a-trusted-server
공식 코드Private Federated Learning Without a Trusted Server: Optimal Algorithms for Convex Losses
https://github.com/lss1242/coevo_bandit_learning
공식 코드Randomised Optimism via Competitive Co-Evolution for Matrix Games with Bandit Feedback
https://github.com/lsu-raise-lab/dplib
공식 코드DPLib: A Standard Benchmark Library for Distributed Power System Analysis and Optimization
https://github.com/ludvins/variational-lla
공식 코드Variational Linearized Laplace Approximation for Bayesian Deep Learning
https://github.com/luoxinlongroger/cnmge
공식 코드Continuation Newton methods with deflation techniques for global optimization problems
https://github.com/madkn/HistogramLoss
공식 코드https://github.com/mahdihosseini/rmsgd
공식 코드https://github.com/manuelmlmadeira/coco-denoiser
공식 코드COCO Denoiser: Using Co-Coercivity for Variance Reduction in Stochastic Convex Optimization
https://github.com/mark-h-meng/nnprune
공식 코드Supervised Robustness-preserving Data-free Neural Network Pruning
https://github.com/martimbrandao/legopt-benchmark
공식 코드https://github.com/masa-ue/prodifevo-refinement
공식 코드https://github.com/mascarenhasav/AbCD
공식 코드AbCD: A Component-wise Adjustable Framework for Dynamic Optimization Problems
https://github.com/mathgerberch/gpfso
공식 코드https://github.com/mathieugerber/PFoptim
공식 코드https://github.com/mathieugerber/gpfso-paper
공식 코드https://github.com/mavridischristos/onlinedeterministicannealing
공식 코드Annealing Optimization for Progressive Learning with Stochastic Approximation
https://github.com/maximilienlc/nra
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.