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Distributed Optimization
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The goal of Distributed Optimization is to optimize a certain objective defined over millions of billions of data that is distributed over many machines by utilizing the computational power of these machines.
Source: Analysis of Distributed StochasticDual Coordinate Ascent
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/BaguaSys/bagua
공식 코드BAGUA: Scaling up Distributed Learning with System Relaxations
https://github.com/CMU-SAFARI/PIM-Opt
공식 코드https://github.com/HikariX/MP-FedXGB
공식 코드https://github.com/MLOPTPSU/FedTorch
공식 코드Federated Learning with Compression: Unified Analysis and Sharp Guarantees
https://github.com/NikolaKon1994/Robust-Learning-from-Untrusted-Sources
공식 코드https://github.com/WilliamYi96/EF-BV
공식 코드https://github.com/WilliamYi96/VR-ProxSkip
공식 코드Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning
https://github.com/XiaoFuLab/federated_max_var_gcca
공식 코드Communication-Efficient Federated Linear and Deep Generalized Canonical Correlation Analysis
https://github.com/YeTian-93/OPTRA
공식 코드Accelerated Primal-Dual Algorithms for Distributed Smooth Convex Optimization over Networks
https://github.com/ZhishuaiGuo/DistributedAUC
공식 코드Communication-Efficient Distributed Stochastic AUC Maximization with Deep Neural Networks
https://github.com/adap/flower
공식 코드https://github.com/alexliuyuren/zoopt_experiment
공식 코드https://github.com/artomaranjyan/GradSkip-code
공식 코드GradSkip: Communication-Accelerated Local Gradient Methods with Better Computational Complexity
https://github.com/bentoayr/distributed-opt-and-topology
공식 코드Distributed Optimization, Averaging via ADMM, and Network Topology
https://github.com/burlachenkok/ef21_with_rare_features
공식 코드https://github.com/chenshixiang/Decentralized_Riemannian_gradient_descent_on_Stiefel_manifold
공식 코드Decentralized Riemannian Gradient Descent on the Stiefel Manifold
https://github.com/cvxgrp/optimization_via_circuits
공식 코드https://github.com/cvxgrp/signal-decomposition
공식 코드https://github.com/dat-2022/dat
공식 코드Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
https://github.com/edouardoyallon/acco
공식 코드ACCO: Accumulate While You Communicate for Communication-Overlapped Sharded LLM Training
https://github.com/epfml/byzantine-robust-noniid-optimizer
공식 코드Byzantine-Robust Learning on Heterogeneous Datasets via Bucketing
https://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/eyounx/ZOOpt
공식 코드https://github.com/ezelikman/justonebyte
공식 코드https://github.com/facebookresearch/fairscale/blob/main/fairscale/experimental/nn/data_parallel/gossip/distributed.py
공식 코드SlowMo: Improving Communication-Efficient Distributed SGD with Slow Momentum
https://github.com/gingsmith/cocoa
공식 코드Adding vs. Averaging in Distributed Primal-Dual Optimization
https://github.com/gingsmith/proxcocoa
공식 코드L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
https://github.com/google-deepmind/asyncdiloco
공식 코드https://github.com/gundoganalperen/DIRAL
공식 코드https://github.com/hanguo97/expectation-propagation
공식 코드Federated Learning as Variational Inference: A Scalable Expectation Propagation Approach
https://github.com/hongliny/FedAc-NeurIPS20
공식 코드https://github.com/iclr-paper/btard
공식 코드https://github.com/ist-daslab/microadam
공식 코드MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.