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/facebookresearch/DomainBed
https://github.com/facebookresearch/cutler
https://github.com/facebookresearch/fairscale
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/facebookresearch/moco-v3
https://github.com/fachu000/non_cooperative_aerial_base_station_placement
Non-cooperative Aerial Base Station Placement via Stochastic Optimization
https://github.com/fairfl/FCFL
https://github.com/fairfl/FUEL
https://github.com/fanq15/FSOD-code
https://github.com/fastalgo/imagenet_resnet50_lamb
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/fin-young/THEO_POULA
https://github.com/finitearth/promptolution
EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
https://github.com/fisherd3/fdm
https://github.com/float256/rectified-adam-keras
https://github.com/frankecurtis/StochasticSQP
Sequential Quadratic Optimization for Nonlinear Equality Constrained Stochastic Optimization
https://github.com/frgfm/Holocron
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/galdeia/feat
Learning concise representations for regression by evolving networks of trees
https://github.com/gaoliang13/feddc
https://github.com/gd-zhang/noisy-K-FAC
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/geoopt/geoopt
https://github.com/giannifranchi/NAO_morpho
https://github.com/google-deepmind/kfac-jax
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/google-deepmind/optax
Benchopt: Reproducible, efficient and collaborative optimization benchmarks
https://github.com/google-research/public-data-in-dpfl
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/gpauloski/kfac_pytorch
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/greyhound101/IEEE-CIS-Fraud
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/grtzsohalf/pt.darts
https://github.com/guanxiongsun/stpn
https://github.com/gurucharanmk/PyTorch_CosineAnnealingWithRestartsLR
https://github.com/hannibal046/pluglm
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/haoxiang-wang/isr
https://github.com/harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Algorithms
https://github.com/harshraj11584/Paper-Implementation-Overview-Gradient-Descent-Optimization-Sebastian-Ruder
https://github.com/haziqj/ubd-bgtvi
https://github.com/helw150/levanter
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
https://github.com/hmgxr128/local-sgd
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.