https://github.com/hadijomaa/HypRL
공식 코드Hyp-RL : Hyperparameter Optimization by Reinforcement Learning
Papers with Code 아카이브
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/hadijomaa/dataset2vec
공식 코드https://github.com/haihabi/GeneticNAS
공식 코드https://github.com/halimabouzidi/hadas
공식 코드HADAS: Hardware-Aware Dynamic Neural Architecture Search for Edge Performance Scaling
https://github.com/hamidreza-s-salehi/BrainMetDetect
공식 코드https://github.com/han-cai/EAS
공식 코드https://github.com/han-cai/PathLevel-EAS
공식 코드Path-Level Network Transformation for Efficient Architecture Search
https://github.com/han-cai/RL4AS_NetTrans
공식 코드https://github.com/hayeonlee/help
공식 코드HELP: Hardware-Adaptive Efficient Latency Prediction for NAS via Meta-Learning
https://github.com/hbp-unibi/SNABSuite
공식 코드Benchmarking Deep Spiking Neural Networks on Neuromorphic Hardware
https://github.com/he-y/awesome-pruning
공식 코드Structured Pruning for Deep Convolutional Neural Networks: A survey
https://github.com/herilalaina/mosaic
공식 코드https://github.com/hkbu-hpml/easnet
공식 코드EASNet: Searching Elastic and Accurate Network Architecture for Stereo Matching
https://github.com/hkzhang91/3D-ANAS
공식 코드3D-ANAS: 3D Asymmetric Neural Architecture Search for Fast Hyperspectral Image Classification
https://github.com/hkzhang91/hinas
공식 코드Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
https://github.com/hp-transfer/htaa_experiments
공식 코드https://github.com/hpnair/18663_Project_FBNet
공식 코드https://github.com/hsg-aiml/neurips_2021-weight_space_learning
공식 코드Hyper-Representations for Pre-Training and Transfer Learning
https://github.com/hsg-aiml/neurips_2022-generative_hyper_representations
공식 코드Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights
https://github.com/huawei-noah/CARS
공식 코드CARS: Continuous Evolution for Efficient Neural Architecture Search
https://github.com/huawei-noah/Efficient-Computing
공식 코드https://github.com/huawei-noah/Pretrained-Language-Model
공식 코드AutoTinyBERT: Automatic Hyper-parameter Optimization for Efficient Pre-trained Language Models
https://github.com/huawei-noah/hebo
공식 코드HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
https://github.com/huawei-noah/noah-research/tree/master/BO/HEBO
공식 코드HEBO Pushing The Limits of Sample-Efficient Hyperparameter Optimisation
https://github.com/huawei-noah/vega
공식 코드https://github.com/huawei-noah/vega/tree/master/examples/nas/nago
공식 코드https://github.com/hujiecpp/NAD
공식 코드https://github.com/human-analysis/MUXConv
공식 코드MUXConv: Information Multiplexing in Convolutional Neural Networks
https://github.com/human-analysis/neural-architecture-transfer
공식 코드https://github.com/hust-linyi/LENAS
공식 코드LENAS: Learning-based Neural Architecture Search and Ensemble for 3D Radiotherapy Dose Prediction
https://github.com/hustvl/EfficientPose
공식 코드EfficientPose: Efficient Human Pose Estimation with Neural Architecture Search
https://github.com/hy-he/is-darts
공식 코드IS-DARTS: Stabilizing DARTS through Precise Measurement on Candidate Importance
https://github.com/hyeonjeongha/croze
공식 코드Generalizable Lightweight Proxy for Robust NAS against Diverse Perturbations
https://github.com/hynkis/MIHO
공식 코드https://github.com/hynkis/mi-hpo
공식 코드https://github.com/hzf1174/robot
공식 코드Robustifying and Boosting Training-Free Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.