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AutoML
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Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/geoalgo/syne-tune
공식 코드Optimizing Hyperparameters with Conformal Quantile Regression
https://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/fair-automl
공식 코드Fix Fairness, Don't Ruin Accuracy: Performance Aware Fairness Repair using AutoML
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/gjoelbye/patternlocal
공식 코드Minimizing False-Positive Attributions in Explanations of Non-Linear Models
https://github.com/gmatilde/vdrnn
공식 코드Probabilistic Rollouts for Learning Curve Extrapolation Across Hyperparameter Settings
https://github.com/gmh14/RobNets
공식 코드When NAS Meets Robustness: In Search of Robust Architectures against Adversarial Attacks
https://github.com/gohypergiant/research-autodc
공식 코드https://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/automl_zero
공식 코드AutoML-Zero: Evolving Machine Learning Algorithms From Scratch
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/optformer
공식 코드Towards Learning Universal Hyperparameter Optimizers with Transformers
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
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/google/compare_gan
공식 코드https://github.com/google/vizier
공식 코드https://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/guitaricet/peft_comparison
공식 코드Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-Tuning
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
https://github.com/guoyongcs/HNAS-SR
공식 코드Hierarchical Neural Architecture Search for Single Image Super-Resolution
https://github.com/guoyongcs/NAT
공식 코드NAT: Neural Architecture Transformer for Accurate and Compact Architectures
https://github.com/guoyongcs/NATv2
공식 코드Towards Accurate and Compact Architectures via Neural Architecture Transformer
https://github.com/hadarshavit/nexception
공식 코드From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.