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Neural Architecture Search
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Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/fairnas/FairNAS
공식 코드FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
https://github.com/falsr/FALSR
공식 코드Fast, Accurate and Lightweight Super-Resolution with Neural Architecture Search
https://github.com/fastmachinelearning/nac-opt
공식 코드https://github.com/fdbtrs/pocketnet
공식 코드https://github.com/fei-hdu/NAS-Lung
공식 코드https://github.com/flairox/behaviour-distillation
공식 코드https://github.com/flanders-make-vzw/hpc2edge
공식 코드Optimizing edge AI models on HPC systems with the edge in the loop
https://github.com/fmsnew/nas-bench-nlp-release
공식 코드NAS-Bench-NLP: Neural Architecture Search Benchmark for Natural Language Processing
https://github.com/fxsxjtu/grato
공식 코드GraTO: Graph Neural Network Framework Tackling Over-smoothing with Neural Architecture Search
https://github.com/gabikadlecova/zc_combine
공식 코드Surprisingly Strong Performance Prediction with Neural Graph Features
https://github.com/gaia-vision/gaia-ssl
공식 코드https://github.com/gatech-eic/nasa
공식 코드NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
https://github.com/gauravfs-14/awesome-tinyml
공식 코드https://github.com/gdkent/bsg_methods_con_unc
공식 코드Inexact bilevel stochastic gradient methods for constrained and unconstrained lower-level problems
https://github.com/geifmany/Active-inas
공식 코드https://github.com/georgehc/survival-kernets
공식 코드https://github.com/georgejolo/enfusion
공식 코드https://github.com/ggjy/HitDet.pytorch
공식 코드Hit-Detector: Hierarchical Trinity Architecture Search for Object Detection
https://github.com/giangnm58/manas
공식 코드https://github.com/giannifranchi/NAO_morpho
공식 코드Learning Deep Morphological Networks with Neural Architecture Search
https://github.com/gideonfr/emprox
공식 코드EmProx: Neural Network Performance Estimation For Neural Architecture Search
https://github.com/giuliotosato/autokeras-bioacustic
공식 코드https://github.com/gmh14/RobNets
공식 코드When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
https://github.com/gomerudo/nas-env
공식 코드Learning to reinforcement learn for Neural Architecture Search
https://github.com/google-research/google-research
공식 코드Can weight sharing outperform random architecture search? An investigation with TuNAS
https://github.com/google-research/google-research/tree/master/nngp_nas
공식 코드https://github.com/google-research/nasbench
공식 코드NAS-Bench-101: Towards Reproducible Neural Architecture Search
https://github.com/google-research/tabnas
공식 코드TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets
https://github.com/google-research/unified_functional_hashing
공식 코드https://github.com/google/automl/tree/master/efficientnetv2
공식 코드https://github.com/google/brain_autorl
공식 코드Differentiable Architecture Search for Reinforcement Learning
https://github.com/great8nctu/fox-nas
공식 코드FOX-NAS: Fast, On-device and Explainable Neural Architecture Search
https://github.com/guillaume-michel/dvolver
공식 코드DVOLVER: Efficient Pareto-Optimal Neural Network Architecture Search
https://github.com/guochengqian/tnas
공식 코드When NAS Meets Trees: An Efficient Algorithm for Neural Architecture Search
https://github.com/guoyongcs/CNAS
공식 코드Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.