https://github.com/aiiu-lab/DemonSampling
공식 코드Training-free Diffusion Model Alignment with Sampling Demons
Papers with Code 아카이브
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/aiqz/bype-vae
공식 코드https://github.com/ajjimeno/fast-evolutionary-evaluation
공식 코드Evolutionary thoughts: integration of large language models and evolutionary algorithms
https://github.com/ajjimeno/list-data
공식 코드Evolutionary thoughts: integration of large language models and evolutionary algorithms
https://github.com/ajjimeno/llm-gp
공식 코드Evolutionary thoughts: integration of large language models and evolutionary algorithms
https://github.com/albietz/ppsgd
공식 코드Personalization Improves Privacy-Accuracy Tradeoffs in Federated Learning
https://github.com/albietz/stochs
공식 코드Stochastic Optimization with Variance Reduction for Infinite Datasets with Finite-Sum Structure
https://github.com/alephzr/ie-gan
공식 코드https://github.com/alexliuyuren/zoopt_experiment
공식 코드https://github.com/aliborji/nke
공식 코드https://github.com/alpyurtsever/SHCGM
공식 코드https://github.com/amidos2006/gym-pcgrl
공식 코드https://github.com/amirgholami/adahessian
공식 코드ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning
https://github.com/anders-s-olsen/coupled-generator-decomposition
공식 코드Coupled generator decomposition for fusion of electro- and magnetoencephalography data
https://github.com/andyjm3/RSDM
공식 코드Efficient Optimization with Orthogonality Constraint: a Randomized Riemannian Submanifold Method
https://github.com/animadversio/actmax-optimizer-dev
공식 코드High-performance Evolutionary Algorithms for Online Neuron Control
https://github.com/antoniobarbalau/black-box-ripper
공식 코드Black-Box Ripper: Copying black-box models using generative evolutionary algorithms
https://github.com/anuraganands/Single-objective-Bi-level-with-ICHEA--BICHEA-
공식 코드Optimistic variants of single-objective bilevel optimization for evolutionary algorithms
https://github.com/arm-software/shackleton-framework
공식 코드Optimizing LLVM Pass Sequences with Shackleton: A Linear Genetic Programming Framework
https://github.com/arthurcerveira/mtdd-evaluation-framework
공식 코드https://github.com/artomaranjyan/GradSkip-code
공식 코드GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
https://github.com/aspuru-guzik-group/janus
공식 코드https://github.com/attilanagy234/enas-pytorch
공식 코드Improving the sample-efficiency of neural architecture search with reinforcement learning
https://github.com/auroua/NPENASv1
공식 코드NPENAS: Neural Predictor Guided Evolution for Neural Architecture Search
https://github.com/awslabs/or-rl-benchmarks
공식 코드ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
https://github.com/aymericvie/networked-genetic-algorithms
공식 코드Population network structure impacts genetic algorithm optimisation performance
https://github.com/balasrini33/JanossyPooling
공식 코드Janossy Pooling: Learning Deep Permutation-Invariant Functions for Variable-Size Inputs
https://github.com/baofff/BiSM
공식 코드Bi-level Score Matching for Learning Energy-based Latent Variable Models
https://github.com/baojianzhou/graph-sto-iht
공식 코드Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization
https://github.com/basvanstein/ioh-explainer
공식 코드Explainable Benchmarking for Iterative Optimization Heuristics
https://github.com/benchopt/benchopt
공식 코드Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/benjidupuis/heavy_tails_generalization
공식 코드Generalization Bounds for Heavy-Tailed SDEs through the Fractional Fokker-Planck Equation
https://github.com/bentoayr/distributed-opt-and-topology
공식 코드Distributed Optimization, Averaging via ADMM, and Network Topology
https://github.com/bghojogh/LBFGS-Vector-Transport-Free
공식 코드https://github.com/blei-lab/ars-reparameterization
공식 코드Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
https://github.com/bokun-wang/moml
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.