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/201419/Optimizer-PyTorch
https://github.com/2023-MindSpore-1/ms-code-215/tree/main/pnasnet
https://github.com/Abdoulaye-Koroko/natural-gradients
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
https://github.com/Abhimanyu08/Lookahead_Optimizer
https://github.com/Adirlou/OptML_Project
Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication
https://github.com/AfuroZamurai/InformatiCup_Leibniz-7
Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images
https://github.com/AlanFokCo/compensation-tools
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/AlbertUW807/DLNN-Algo
https://github.com/AlexandruBurlacu/keras_squeezenet
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
https://github.com/Alsr96/LMMAES
Limited-Memory Matrix Adaptation for Large Scale Black-box Optimization
https://github.com/AnirudhMaiya/RAdam
https://github.com/Ankur3107/awesome-daily-blog
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/AntoAndGar/task_singular_vectors
https://github.com/Anunay1234/Sentiment-Analysis-using-LSTM
https://github.com/AshwinRJ/Federated-Learning-PyTorch
Federated Learning: Challenges, Methods, and Future Directions
https://github.com/BRAIN-chain/FRAIN
Federated Learning: Challenges, Methods, and Future Directions
https://github.com/BUPT-PRIV/MAE-priv
https://github.com/BarbaAlGhul/recommender-system-thesis
https://github.com/Bibyutatsu/Self_Driving_Car
https://github.com/Byte7/Adaptative-Curriculum-GAN-keras
https://github.com/Byte7/Adaptive-Curriculum-GAN-keras
https://github.com/COMP6248-Reproducability-Challenge/LookaheadOptimizer
https://github.com/Cadene/pretrained-models.pytorch
https://github.com/Capsar/dat
Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
https://github.com/CharlieDinh/FEDL_pytorch
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
https://github.com/Cheese-5040/OCT-Scan-Segmentation
https://github.com/ChengBinJin/Adam-Analysis-TensorFlow
https://github.com/CoinCheung/denseCL
https://github.com/Coolgiserz/NLP_starter
https://github.com/DAve-QN/source
Communication Efficient Distributed Optimization using an Approximate Newton-type Method
https://github.com/DChaushev/Age-Gender-Recognition
https://github.com/Daniil-Selikhanovych/Shampoo_optimizer
https://github.com/DarshanDeshpande/tf-madgrad
https://github.com/DataCanvasIO/Hypernets
Regularized Evolution for Image Classifier Architecture Search
https://github.com/DataXujing/PNASNet_pytorch
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.