https://github.com/boluoweifenda/WAGE
공식 코드Training Quantized Deep Neural Networks via Cooperative Coevolution
Papers with Code 아카이브
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/borbysh/coolmomentum
공식 코드CoolMomentum: A Method for Stochastic Optimization by Langevin Dynamics with Simulated Annealing
https://github.com/boschresearch/DD_OPG
공식 코드https://github.com/boxinz17/fl-client-sampling
공식 코드Adaptive Client Sampling in Federated Learning via Online Learning with Bandit Feedback
https://github.com/bremen79/cocob
공식 코드Training Deep Networks without Learning Rates Through Coin Betting
https://github.com/brett-daley/expectigrad
공식 코드Expectigrad: Fast Stochastic Optimization with Robust Convergence Properties
https://github.com/brianbullins/lissa_code
공식 코드Second-Order Stochastic Optimization for Machine Learning in Linear Time
https://github.com/bsvineethiitg/adams
공식 코드Exploiting Uncertainty of Loss Landscape for Stochastic Optimization
https://github.com/burkh4rt/filtered-stochastic-newton
공식 코드https://github.com/burlachenkok/ef21_with_rare_features
공식 코드https://github.com/by1tTZ4IsQkAO80F/iclr_2019
공식 코드Learning concise representations for regression by evolving networks of trees
https://github.com/cambrian-org/ACI
공식 코드https://github.com/caroladoerr/leadingonedac
공식 코드Theory-inspired Parameter Control Benchmarks for Dynamic Algorithm Configuration
https://github.com/carranza96/waymo-detection-optimization
공식 코드https://github.com/cchangyou/Santa
공식 코드Bridging the Gap between Stochastic Gradient MCMC and Stochastic Optimization
https://github.com/chenshixiang/Decentralized_Riemannian_gradient_descent_on_Stiefel_manifold
공식 코드Decentralized Riemannian Gradient Descent on the Stiefel Manifold
https://github.com/chenxi116/PNASNet.TF
공식 코드https://github.com/chenxi116/PNASNet.pytorch
공식 코드https://github.com/cicirello/cycle-mutation-experiments
공식 코드https://github.com/cicirello/permutation-crossover-landscape-analysis
공식 코드A Survey and Analysis of Evolutionary Operators for Permutations
https://github.com/clint-kristopher-morris/llm-guided-evolution
공식 코드LLM Guided Evolution - The Automation of Models Advancing Models
https://github.com/cognizant-ai-labs/sepx-paper
공식 코드https://github.com/comp-imaging-sci/mining-tomo-solutions-pulse
공식 코드https://github.com/cross32768/PlaNet_PyTorch
공식 코드Stochastic Gradient Descent Captures How Children Learn About Physics
https://github.com/csanhuezalobos/gar60
공식 코드https://github.com/cschen1205/lmea
공식 코드https://github.com/cumberkid/learning-the-optimal-solution-path
공식 코드https://github.com/cvxgrp/optimization_via_circuits
공식 코드https://github.com/cvxgrp/signal-decomposition
공식 코드https://github.com/cwcleghornai/cornn
공식 코드A Continuous Optimisation Benchmark Suite from Neural Network Regression
https://github.com/dadopuccio/mb-conjugate-gradient-dp
공식 코드https://github.com/daniel-dodd/riemannian_dog
공식 코드Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
https://github.com/danleonte/ambit_stochastics
공식 코드Likelihood-based inference and forecasting for trawl processes: a stochastic optimization approach
https://github.com/dat-2022/dat
공식 코드Distributed Adversarial Training to Robustify Deep Neural Networks at Scale
https://github.com/dccspeed/rgpm
공식 코드Graph Pattern Mining and Learning through User-defined Relations (Extended Version)
https://github.com/deeplearningmethods/averaged-adam
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.