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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/mec-lab/SRNE
공식 코드Evolving Form and Function: Dual-Objective Optimization in Neural Symbolic Regression Networks
https://github.com/mertan-a/pollination
공식 코드https://github.com/mhnnunes/nas_gnn
공식 코드https://github.com/mhw32/antithetic-vae-public
공식 코드Differentiable Antithetic Sampling for Variance Reduction in Stochastic Variational Inference
https://github.com/mia-katrin/modbots
공식 코드Centralized and Decentralized Control in Modular Robots and Their Effect on Morphology
https://github.com/michaelrzhang/lookahead
공식 코드https://github.com/mickeyhqian/voteensemble
공식 코드Subsampled Ensemble Can Improve Generalization Tail Exponentially
https://github.com/mihaioltean/evolve-algorithms
공식 코드Evolving Evolutionary Algorithms using Multi Expression Programming
https://github.com/miltonllera/meta-evolved-dev
공식 코드https://github.com/mlolab/motef
공식 코드Towards Faster Decentralized Stochastic Optimization with Communication Compression
https://github.com/mmkamani7/LUPA-SGD
공식 코드Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization
https://github.com/mohammed-elkomy/stochastic-fractal-search-python
공식 코드https://github.com/mopsd/sgd_gmm
공식 코드Stochastic First-Order Learning for Large-Scale Flexibly Tied Gaussian Mixture Model
https://github.com/mpotto/pyselect
공식 코드RFFNet: Large-Scale Interpretable Kernel Methods via Random Fourier Features
https://github.com/mudita11/Tune-AOS-bbob
공식 코드Unified Framework for the Adaptive Operator Selection of Discrete Parameters
https://github.com/muellerlab/synthesizing_interpretable_control_policies
공식 코드Synthesizing Interpretable Control Policies through Large Language Model Guided Search
https://github.com/mulgarfed/minimax_dssog
공식 코드https://github.com/nandofioretto/GpuBE
공식 코드Accelerating Exact and Approximate Inference for (Distributed) Discrete Optimization with GPUs
https://github.com/ncclabsustech/MOVEA
공식 코드https://github.com/nesl/adversarial_genattack
공식 코드GenAttack: Practical Black-box Attacks with Gradient-Free Optimization
https://github.com/nightstorm0909/nevonas
공식 코드https://github.com/nightstorm0909/pevonas
공식 코드https://github.com/nikitinvv/tomoalign
공식 코드https://github.com/nikolausdemmel/rootba
공식 코드https://github.com/nikosimus/br-lsvrg
공식 코드Byzantine-Robust Loopless Stochastic Variance-Reduced Gradient
https://github.com/nikosimus/cc-for-br-learning
공식 코드Communication Compression for Byzantine Robust Learning: New Efficient Algorithms and Improved Rates
https://github.com/ninja-wm/pom
공식 코드Pretrained Optimization Model for Zero-Shot Black Box Optimization
https://github.com/ninokfox/rea
공식 코드https://github.com/nliulab/sim-shapley
공식 코드SIM-Shapley: A Stable and Computationally Efficient Approach to Shapley Value Approximation
https://github.com/nobuo-namura/engineering-benchmark-problem
공식 코드Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
https://github.com/nobuo-namura/hpa
공식 코드Single and Multi-Objective Optimization Benchmark Problems Focusing on Human-Powered Aircraft Design
https://github.com/nrel/de
공식 코드From Text to Test: AI-Generated Control Software for Materials Science Instruments
https://github.com/nrel/keithley_gpt
공식 코드From Text to Test: AI-Generated Control Software for Materials Science Instruments
https://github.com/nvlabs/red-diff
공식 코드A Variational Perspective on Solving Inverse Problems with Diffusion Models
https://github.com/oknakfm/dpd
공식 코드Minimizing robust density power-based divergences for general parametric density models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.