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/sjiang95/semcl
https://github.com/skanda99/Simple-Neural-Networks
https://github.com/skyday123/pytorch-lamb
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/smearle/control-pcgrl
https://github.com/smearle/gym-pcgrl
https://github.com/sophieburkhardt/dirichlet-vae-topic-models
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms
https://github.com/soran-ghaderi/torchebm
A Contrastive Divergence for Combining Variational Inference and MCMC
https://github.com/ssagawa/overparam_spur_corr
https://github.com/stanford-crfm/levanter
Sophia: A Scalable Stochastic Second-order Optimizer for Language Model Pre-training
https://github.com/stephenbeckr/AIMS
https://github.com/stephenbeckr/CambridgeOptimisationCourse
https://github.com/superkido511/AdaMod-Keras
An Adaptive and Momental Bound Method for Stochastic Learning
https://github.com/suryabulusu/VCD
A Contrastive Divergence for Combining Variational Inference and MCMC
https://github.com/suzyi/optimization
https://github.com/tally0818/NASNet
Regularized Evolution for Image Classifier Architecture Search
https://github.com/taohu88/BayesianML
https://github.com/taolicheng/Deep-Learning-Learning-Path
https://github.com/tensorflow/addons/blob/master/tensorflow_addons/optimizers/cocob.py
Training Deep Networks without Learning Rates Through Coin Betting
https://github.com/tensorflow/kfac
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/tensorflow/models/tree/master/research/deeplab
https://github.com/tensorflow/models/tree/master/research/inception
https://github.com/tensorflow/models/tree/master/research/slim
https://github.com/tensorflow/tpu/tree/master/models/official/amoeba_net
Regularized Evolution for Image Classifier Architecture Search
https://github.com/tensorpack/benchmarks
https://github.com/tfjgeorge/nngeometry
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/thejungwon/gc-fed
SCAFFOLD: Stochastic Controlled Averaging for Federated Learning
https://github.com/theneuralbeing/neuroevolution
https://github.com/theogf/DeterminantalPointProcesses.jl
https://github.com/thomasWeise/jsspInstancesAndResults
Boosting Binary Optimization via Binary Classification: A Case Study of Job Shop Scheduling
https://github.com/tinagui/Swarm-Intelligence-in-Bioinformatics
A swarm optimization algorithm inspired in the behavior of the social-spider
https://github.com/titu1994/progressive-neural-architecture-search
https://github.com/tmbluth/Distracted-Drivers-Detection
https://github.com/tmulc18/Distributed-TensorFlow-Guide
https://github.com/tomkimpson/ml4l
https://github.com/tqch/esgd-ws
Evolutionary Stochastic Gradient Descent for Optimization of Deep Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.