https://github.com/Rondorf/BOReL
공식 코드Adapting to Mixing Time in Stochastic Optimization with Markovian Data
Papers with Code 아카이브
Stochastic Optimization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 12
- 보존된 평가 기록
- 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/Rqcker/SymbioticAIS
공식 코드SAIS: A Novel Bio-Inspired Artificial Immune System Based on Symbiotic Paradigm
https://github.com/RyanCV/BPGrad
공식 코드https://github.com/SHIXOOM/PAIR
공식 코드PAIR: A Novel Large Language Model-Guided Selection Strategy for Evolutionary Algorithms
https://github.com/SamanKhamesian/Hybrid-Self-Attention-NEAT
공식 코드Hybrid Self-Attention NEAT: A novel evolutionary approach to improve the NEAT algorithm
https://github.com/SamuelHorvath/Compressed_SGD_PyTorch
공식 코드A Better Alternative to Error Feedback for Communication-Efficient Distributed Learning
https://github.com/ShawK91/erl_paper_nips18
공식 코드https://github.com/TalkingData/Fregata
공식 코드Greedy Step Averaging: A parameter-free stochastic optimization method
https://github.com/Tarzanagh/DADAM
공식 코드DADAM: A Consensus-based Distributed Adaptive Gradient Method for Online Optimization
https://github.com/ThayaFluss/cnl
공식 코드https://github.com/TrueNobility303/DREAM
공식 코드An Efficient Stochastic Algorithm for Decentralized Nonconvex-Strongly-Concave Minimax Optimization
https://github.com/TrueNobility303/RAIN
공식 코드Near-Optimal Algorithms for Making the Gradient Small in Stochastic Minimax Optimization
https://github.com/Weiyu-USTC/CSGD
공식 코드Communication-Censored Distributed Stochastic Gradient Descent
https://github.com/Wenlin-Chen/geometric-parameterization
공식 코드Neural Characteristic Activation Analysis and Geometric Parameterization for ReLU Networks
https://github.com/WilliamYi96/EF-BV
공식 코드https://github.com/WilliamYi96/VR-ProxSkip
공식 코드Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
https://github.com/XiaoFuLab/federated_max_var_gcca
공식 코드Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
https://github.com/XuezheMax/apollo
공식 코드https://github.com/Y1fanHE/po_with_moead-levy
공식 코드Solving Portfolio Optimization Problems Using MOEA/D and Levy Flight
https://github.com/YeTian-93/OPTRA
공식 코드Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
https://github.com/YosefLab/scVI
공식 코드A deep generative model for gene expression profiles from single-cell RNA sequencing
https://github.com/Zhangyanbo/diffusion-evolution
공식 코드https://github.com/ZhishuaiGuo/DistributedAUC
공식 코드Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
https://github.com/a1708192/WDSOP
공식 코드Covariance Matrix Adaptation Greedy Search Applied to Water Distribution System Optimization
https://github.com/aaupadhy/AmCLR
공식 코드AmCLR: Unified Augmented Learning for Cross-Modal Representations
https://github.com/acoache/rl-dynamicconvexrisk
공식 코드https://github.com/acsicuib/ga4fogplacement
공식 코드https://github.com/adap/flower
공식 코드https://github.com/adaptive-intelligent-robotics/AURORA
공식 코드Unsupervised Behaviour Discovery with Quality-Diversity Optimisation
https://github.com/adaptive-intelligent-robotics/DCRL-MAP-Elites
공식 코드Synergizing Quality-Diversity with Descriptor-Conditioned Reinforcement Learning
https://github.com/adaptive-intelligent-robotics/dominated-novelty-search
공식 코드Dominated Novelty Search: Rethinking Local Competition in Quality-Diversity
https://github.com/adaptive-intelligent-robotics/mome_pgx
공식 코드https://github.com/aheuillet/nasiam
공식 코드NASiam: Efficient Representation Learning using Neural Architecture Search for Siamese Networks
https://github.com/ahmed-abouzeid/mmss
공식 코드Socially Fair Mitigation of Misinformation on Social Networks via Constraint Stochastic Optimization
https://github.com/ahmed-elliethy/accel-genetic-non-linear-mpc-learning-optimal-search-space
공식 코드https://github.com/ai4co/llm-as-hh
공식 코드ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.