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/apourchot/ERL-pytorch
https://github.com/apourchot/EvoLearn
https://github.com/appliedAI-Initiative/pyDVL
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/arampacha/clip-rsicd
https://github.com/arminarj/deepgcca-pytorch
https://github.com/artiste-qb-net/Quantum_Edward
https://github.com/awslabs/adatune
https://github.com/batorskq/prl
EvoPrompt: Connecting LLMs with Evolutionary Algorithms Yields Powerful Prompt Optimizers
https://github.com/bdemeshev/om_ts
https://github.com/bdemo/pfedbred_public
https://github.com/benathi/fastswa-semi-sup
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/bethgelab/foolbox/blob/master/foolbox/attacks/gen_attack.py
GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/bluesky314/Cyclical_LR_Scheduler_With_Decay_Pytorch
https://github.com/bojone/keras_lookahead
https://github.com/bojone/tiger
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/bond005/yandex-shifts-weather
https://github.com/booydar/t5-experiments
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/brando130/CSharp_NN
https://github.com/bremen79/parameterfree
Training Deep Networks without Learning Rates Through Coin Betting
https://github.com/brendanxwhitaker/spred
Training Deep Networks without Learning Rates Through Coin Betting
https://github.com/btahir/tensorflow-LAMB
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/buptlwz/mabp
https://github.com/camma-public/hpe-adaptor
https://github.com/carbonati/fl-zoo
https://github.com/cavalab/feat
Learning concise representations for regression by evolving networks of trees
https://github.com/cerndb/dist-keras
https://github.com/changhuixu/LSTM-sentiment-analysis
https://github.com/chanshing/xnes
https://github.com/chihchenghsieh/multimodal-medical-diagnosis-system
https://github.com/chizhu/BDC2019
https://github.com/chuiyunjun/projectCSC413
https://github.com/chunhuililili/mt_dnn
https://github.com/cjc77/gaussian_processes_final_project
https://github.com/cmu-enyac/Renofeation
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/cobypenso/functional_ensemble_distillation
Cyclical Stochastic Gradient MCMC for Bayesian Deep Learning
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.