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Stochastic Optimization
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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/oknakfm/hovr
공식 코드https://github.com/oknakfm/sgdpd
공식 코드Minimizing robust density power-based divergences for general parametric density models
https://github.com/oliversieberling/sdbv-ea
공식 코드https://github.com/openai/deeptype
공식 코드DeepType: Multilingual Entity Linking by Neural Type System Evolution
https://github.com/optimization-for-data-driven-science/FERMI
공식 코드A Stochastic Optimization Framework for Fair Risk Minimization
https://github.com/optimization-for-data-driven-science/f-ferm
공식 코드f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
https://github.com/optml-group/soul
공식 코드SOUL: Unlocking the Power of Second-Order Optimization for LLM Unlearning
https://github.com/optml/CoCoA
공식 코드https://github.com/optsuite/mcpg
공식 코드https://github.com/optsuite/o2o
공식 코드https://github.com/papachristoumarios/dp-distributed-estimation
공식 코드https://github.com/paper-submission-rafa/diversification-code
공식 코드Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees
https://github.com/philipp-andelfinger/discograd
공식 코드Smoothing Methods for Automatic Differentiation Across Conditional Branches
https://github.com/phizaz/cprop
공식 코드CProp: Adaptive Learning Rate Scaling from Past Gradient Conformity
https://github.com/phuoc-hoan-le/binaryvit
공식 코드BinaryViT: Pushing Binary Vision Transformers Towards Convolutional Models
https://github.com/pierlucalanzi/procedural-generation-of-shaders-using-interactive-evolutionary-algorithms
공식 코드A Tool for the Procedural Generation of Shaders using Interactive Evolutionary Algorithms
https://github.com/pietrobarbiero/meco
공식 코드Uncovering Coresets for Classification With Multi-Objective Evolutionary Algorithms
https://github.com/pmorenoz/ContinualGP
공식 코드https://github.com/polixir/zoopt
공식 코드https://github.com/probcomp/ravi-uai-2022
공식 코드Recursive Monte Carlo and Variational Inference with Auxiliary Variables
https://github.com/qiongxiu/PrivacyOptimizationSubspace
공식 코드Privacy-Preserving Distributed Optimization via Subspace Perturbation: A General Framework
https://github.com/qqqyang/shuffle-qudio
공식 코드Shuffle-QUDIO: accelerate distributed VQE with trainability enhancement and measurement reduction
https://github.com/quasiquasar/gta-jobshop-data
공식 코드Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling
https://github.com/rafael-a-monteiro-math/landscape_sketch_and_step
공식 코드Landscape-Sketch-Step: An AI/ML-Based Metaheuristic for Surrogate Optimization Problems
https://github.com/ragavsachdeva/DynTTP
공식 코드The Dynamic Travelling Thief Problem: Benchmarks and Performance of Evolutionary Algorithms
https://github.com/rahulkidambi/AccSGD
공식 코드On the insufficiency of existing momentum schemes for Stochastic Optimization
https://github.com/rajarshisaha95/distoptconstrcomm
공식 코드Efficient Randomized Subspace Embeddings for Distributed Optimization under a Communication Budget
https://github.com/rao-epfl/end2end-so
공식 코드End-to-End Learning for Stochastic Optimization: A Bayesian Perspective
https://github.com/remilepriol/causal-adaptation-speed
공식 코드https://github.com/renato145/DENN
공식 코드Neural Networks in Evolutionary Dynamic Constrained Optimization: Computational Cost and Benefits
https://github.com/renqianluo/NAO
공식 코드https://github.com/renqianluo/NAO_pytorch
공식 코드https://github.com/rezabonyadi/LinearOEC
공식 코드https://github.com/rg223/eps
공식 코드https://github.com/rgiordan/dadvipaper
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.