https://github.com/google/brain-tokyo-workshop
공식 코드Modern Evolution Strategies for Creativity: Fitting Concrete Images and Abstract Concepts
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/guanhuaw/Bjork
공식 코드Efficient approximation of Jacobian matrices involving a non-uniform fast Fourier transform (NUFFT)
https://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/haokunchen245/fedpop
공식 코드https://github.com/harrylui98/zoac-tevc
공식 코드Zeroth-Order Actor-Critic: An Evolutionary Framework for Sequential Decision Problems
https://github.com/hcplu/evolutionary-constrained-reinforcement-learning
공식 코드https://github.com/heal-research/operon
공식 코드https://github.com/hellwigm/RotatedKleeMintyProblem
공식 코드https://github.com/hhyqhh/KAN-EA
공식 코드A First Look at Kolmogorov-Arnold Networks in Surrogate-assisted Evolutionary Algorithms
https://github.com/hhyqhh/Relation
공식 코드https://github.com/hhyqhh/laea
공식 코드Large Language Models as Surrogate Models in Evolutionary Algorithms: A Preliminary Study
https://github.com/hiroyuki-kasai/SGDLibrary
공식 코드SGDLibrary: A MATLAB library for stochastic gradient descent algorithms
https://github.com/hongliny/FedAc-NeurIPS20
공식 코드https://github.com/hoonose/sever
공식 코드https://github.com/hphuongdhsp/Q-Newton-method
공식 코드A fast and simple modification of Newton's method helping to avoid saddle points
https://github.com/hpi-elea/elea
공식 코드ELEA -- Build your own Evolutionary Algorithm in your Browser
https://github.com/hsnamkoong/robustopt
공식 코드https://github.com/hysonlab/learnmmf
공식 코드https://github.com/hyzhang98/sgnn
공식 코드Decouple Graph Neural Networks: Train Multiple Simple GNNs Simultaneously Instead of One
https://github.com/icaros-usc/dqd
공식 코드https://github.com/icaros-usc/pyribs
공식 코드https://github.com/iclr-paper/btard
공식 코드https://github.com/ilnura/lb_sgd
공식 코드Log Barriers for Safe Black-box Optimization with Application to Safe Reinforcement Learning
https://github.com/intellhave/ProBLM
공식 코드https://github.com/intlyc/mto-platform
공식 코드MToP: A MATLAB Optimization Platform for Evolutionary Multitasking
https://github.com/iolloj/pasoa
공식 코드https://github.com/ist-daslab/microadam
공식 코드MicroAdam: Accurate Adaptive Optimization with Low Space Overhead and Provable Convergence
https://github.com/isvy08/l2c
공식 코드Feature-based Learning for Diverse and Privacy-Preserving Counterfactual Explanations
https://github.com/jakobbossek/GECCO2021-knapsack-diversity
공식 코드https://github.com/jamaltoutouh/eel-gans
공식 코드Re-purposing Heterogeneous Generative Ensembles with Evolutionary Computation
https://github.com/jamesvuc/KGD
공식 코드Kalman Gradient Descent: Adaptive Variance Reduction in Stochastic Optimization
https://github.com/jan-schuchardt/learning-to-evolve
공식 코드https://github.com/jaryp/adaptivesemantictokencommunication
공식 코드Adaptive Semantic Token Communication for Transformer-based Edge Inference
https://github.com/jcribeiro98/v-gan
공식 코드Adversarial Subspace Generation for Outlier Detection in High-Dimensional Data
https://github.com/jellychen7/clmea
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.