https://github.com/muupan/chainer-eve
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
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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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates
Disjoint principal component analysis by constrained binary particle swarm optimization
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
Automatically Proving Mathematical Theorems with Evolutionary Algorithms and Proof Assistants
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Federated Learning over Wireless Networks: Convergence Analysis and Resource Allocation
Training Deep Networks without Learning Rates Through Coin Betting
Byzantine-Robust Decentralized Stochastic Optimization over Static and Time-Varying Networks
Optimizing Neural Networks with Kronecker-factored Approximate Curvature
Minimax AUC Fairness: Efficient Algorithm with Provable Convergence
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.