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AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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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/renqianluo/SemiNAS
공식 코드https://github.com/researchmm/CDARTS
공식 코드https://github.com/researchmm/LightTrack
공식 코드LightTrack: Finding Lightweight Neural Networks for Object Tracking via One-Shot Architecture Search
https://github.com/researchmm/NEAS
공식 코드One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space Shrinking
https://github.com/rheasukthanker/spdnetnas
공식 코드https://github.com/rhiever/tpot
공식 코드Automating biomedical data science through tree-based pipeline optimization
https://github.com/riad071/bensparx
공식 코드https://github.com/ricardoknauer/tabmini
공식 코드PMLBmini: A Tabular Classification Benchmark Suite for Data-Scarce Applications
https://github.com/rice-eic/nasa
공식 코드NASA: Neural Architecture Search and Acceleration for Hardware Inspired Hybrid Networks
https://github.com/rice-eic/supertickets
공식 코드https://github.com/richarf22/UniAutoML
공식 코드https://github.com/rintaro-ando-tech/n2m-rsi-demo
공식 코드When Your Own Output Becomes Your Training Data: Noise-to-Meaning Loops and a Formal RSI Trigger
https://github.com/robincugny/autoxai
공식 코드Why Should I Choose You? AutoXAI: A Framework for Selecting and Tuning eXplainable AI Solutions
https://github.com/rohanasthana/dinas
공식 코드Multi-conditioned Graph Diffusion for Neural Architecture Search
https://github.com/rsln-s/importance-of-kernel-bandwidth-in-quantum-machine-learning
공식 코드https://github.com/rtu715/nas-bench-360
공식 코드NAS-Bench-360: Benchmarking Neural Architecture Search on Diverse Tasks
https://github.com/rubinxin/TSE
공식 코드Speedy Performance Estimation for Neural Architecture Search
https://github.com/ruizhong961230/embgo
공식 코드Efficient Multiplayer Battle Game Optimizer for Adversarial Robust Neural Architecture Search
https://github.com/ruizhong961230/llmo
공식 코드Large Language Model Assisted Adversarial Robustness Neural Architecture Search
https://github.com/ruocwang/darts-pt
공식 코드https://github.com/ruocwang/efficient-optimizer-search
공식 코드https://github.com/sacmehta/EdgeNets
공식 코드https://github.com/sadit/SimilaritySearch.jl
공식 코드https://github.com/saintslab/ec-nas-bench
공식 코드EC-NAS: Energy Consumption Aware Tabular Benchmarks for Neural Architecture Search
https://github.com/sanaelotfi/bayesian_model_comparison
공식 코드Bayesian Model Selection, the Marginal Likelihood, and Generalization
https://github.com/sashwatanagolum/elivagar
공식 코드Élivágar: Efficient Quantum Circuit Search for Classification
https://github.com/sbarratt/crossval
공식 코드Optimizing for Generalization in Machine Learning with Cross-Validation Gradients
https://github.com/sbb-gh/prosub
공식 코드Progressive Subsampling for Oversampled Data -- Application to Quantitative MRI
https://github.com/sberbank-ai-lab/AutoMLWhitebox
공식 코드LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
https://github.com/sberbank-ai-lab/lightautoml
공식 코드LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
https://github.com/scalingintelligence/archon
공식 코드Archon: An Architecture Search Framework for Inference-Time Techniques
https://github.com/scikit-learn-contrib/imbalanced-learn
공식 코드Imbalanced-learn: A Python Toolbox to Tackle the Curse of Imbalanced Datasets in Machine Learning
https://github.com/sdl-asu/acla
공식 코드https://github.com/selbosh/p-hacking
공식 코드https://github.com/sense-x/uninet
공식 코드UniNet: Unified Architecture Search with Convolution, Transformer, and MLP
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.