https://github.com/deepmind/spectral_inference_networks
공식 코드Spectral Inference Networks: Unifying Deep and Spectral Learning
Papers with Code 아카이브
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/deng-cy/deep_learning_topology_opt
공식 코드Self-Directed Online Machine Learning for Topology Optimization
https://github.com/derek-fox/graph-scsg-iht
공식 코드Stochastic Variance-Reduced Iterative Hard Thresholding in Graph Sparsity Optimization
https://github.com/devanshgupta160/stochastic-differentially-private-and-fair-learning
공식 코드https://github.com/dice-group/evolearner
공식 코드EvoLearner: Learning Description Logics with Evolutionary Algorithms
https://github.com/dimitris-oik/momsps
공식 코드Stochastic Polyak Step-sizes and Momentum: Convergence Guarantees and Practical Performance
https://github.com/diol-unitn/hn-moea-im
공식 코드Influence Maximization in Hypergraphs using Multi-Objective Evolutionary Algorithms
https://github.com/dixianzhu/midam
공식 코드Provable Multi-instance Deep AUC Maximization with Stochastic Pooling
https://github.com/doerlbh/BanditZoo
공식 코드Evolutionary Multi-Armed Bandits with Genetic Thompson Sampling
https://github.com/doinab/DTN-security
공식 코드https://github.com/dongdongshe/neuzz
공식 코드https://github.com/dongyounglim/etheopoula
공식 코드https://github.com/dptp2022/dptp
공식 코드Reproducibility and Baseline Reporting for Dynamic Multi-objective Benchmark Problems
https://github.com/dtucomputetonia/mavseaoncliff
공식 코드How Well Does the Metropolis Algorithm Cope With Local Optima?
https://github.com/duguodong7/model-evolution
공식 코드https://github.com/duncanmcelfresh/RobustKidneyExchange
공식 코드https://github.com/dylanashley/story-distiller
공식 코드https://github.com/eXascaleInfolab/daor
공식 코드Bridging the Gap between Community and Node Representations: Graph Embedding via Community Detection
https://github.com/edouardoyallon/acco
공식 코드ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
https://github.com/eduardgorbunov/accelerated_clipping
공식 코드Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping
https://github.com/eladrich/seboost
공식 코드SEBOOST - Boosting Stochastic Learning Using Subspace Optimization Techniques
https://github.com/elmahdichayti/linspeedupcode
공식 코드https://github.com/emi-group/evomo
공식 코드GPU-accelerated Evolutionary Many-objective Optimization Using Tensorized NSGA-III
https://github.com/emi-group/evorl
공식 코드EvoRL: A GPU-accelerated Framework for Evolutionary Reinforcement Learning
https://github.com/emi-group/evoxbench
공식 코드A Multi-objective Optimization Benchmark Test Suite for Real-time Semantic Segmentation
https://github.com/emilydolson/phylodiversity-metrics-in-EC-GPTP-2021
공식 코드What can phylogenetic metrics tell us about useful diversity in evolutionary algorithms?
https://github.com/emtiyaz/vadam
공식 코드Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam
https://github.com/epfml/byzantine-robust-noniid-optimizer
공식 코드Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
https://github.com/epfml/byzantine-robust-optimizer
공식 코드https://github.com/epfml/powersgd
공식 코드PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization
https://github.com/epfml/sparsifiedSGD
공식 코드https://github.com/epfml/topology-in-decentralized-learning
공식 코드Beyond spectral gap: The role of the topology in decentralized learning
https://github.com/ericzhao28/multidistributionlearning
공식 코드On-Demand Sampling: Learning Optimally from Multiple Distributions
https://github.com/ermongroup/bcd-nets
공식 코드BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery
https://github.com/ermongroup/neuralsort
공식 코드Stochastic Optimization of Sorting Networks via Continuous Relaxations
https://github.com/ermongroup/subsets
공식 코드Reparameterizable Subset Sampling via Continuous Relaxations
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.