Stochastic Optimization
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 2
- 평가표
- 12
- 보존된 평가 기록
- 35
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/hsvgbkhgbv/TACTHMC
https://github.com/hsvgbkhgbv/Thermostat-assisted-continuously-tempered-Hamiltonian-Monte-Carlo-for-Bayesian-learning
https://github.com/hua-zi/FedCFA
FedCFA: Alleviating Simpson's Paradox in Model Aggregation with Counterfactual Federated Learning
https://github.com/hubbs5/or-gym
ORL: Reinforcement Learning Benchmarks for Online Stochastic Optimization Problems
https://github.com/huchen365/ds
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/huggingface/pytorch-image-models
Adafactor: Adaptive Learning Rates with Sublinear Memory Cost
https://github.com/huybik/CNN-from-first-principle
https://github.com/hydrogo/rainnet
https://github.com/hyerinshelly/RL_COVID-19_Korea
EpidemiOptim: A Toolbox for the Optimization of Control Policies in Epidemiological Models
https://github.com/hysts/pytorch_warmup-scheduler
https://github.com/hyz-xmaster/VarifocalNet
https://github.com/idiap/importance-sampling
https://github.com/ilguyi/optimizers.numpy
https://github.com/intel/openfl
https://github.com/izmailovpavel/contrib_swa_examples
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/izmailovpavel/torch_swa_examples
Averaging Weights Leads to Wider Optima and Better Generalization
https://github.com/jMetal/jMetal
Merge Non-Dominated Sorting Algorithm for Many-Objective Optimization
https://github.com/jae1213/CyclicalLR_SGDR
https://github.com/jaemin-shin/fedbalancer
https://github.com/jameschapman19/cca_zoo
https://github.com/jamesvuc/BBVI
https://github.com/jangsoopark/IDN-TensorFlow
https://github.com/jeff-zilence/transgeo2022
https://github.com/jeffking1998/K-sums
https://github.com/jgarridoalcazar/InhPlastVisualization
https://github.com/jiajunhua/facebookresearch-Detectron
https://github.com/jiaowoguanren0615/MambaVision
Large Batch Optimization for Deep Learning: Training BERT in 76 minutes
https://github.com/jiayueg/multimodal
On Deep Multi-View Representation Learning: Objectives and Optimization
https://github.com/jiechenjiechen/FastGCN-matlab
Stochastic Gradient Descent with Biased but Consistent Gradient Estimators
https://github.com/jinghuichen/fedcams
https://github.com/jj-zhu/kdro
https://github.com/jj-zhu/leibniz-ss-2021
A Kernel Mean Embedding Approach to Reducing Conservativeness in Stochastic Programming and Control
https://github.com/jliphard/DeepEvolve
https://github.com/jmairal/cyanure
Cyanure: An Open-Source Toolbox for Empirical Risk Minimization for Python, C++, and soon more
https://github.com/jolibrain/caffe
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.