https://github.com/RodriguesGabriel/greenmachine
공식 코드GreenMachine: Automatic Design of Zero-Cost Proxies for Energy-Efficient NAS
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
- 4
- 보존된 평가 기록
- 11
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/RolandGao/RegSeg
공식 코드Rethinking Dilated Convolution for Real-time Semantic Segmentation
https://github.com/Rqcker/eai2024
공식 코드https://github.com/SLDGroup/ZiCo
공식 코드ZiCo: Zero-shot NAS via Inverse Coefficient of Variation on Gradients
https://github.com/SNAS-Series/SNAS-Series
공식 코드https://github.com/Sanghyun-Hong/How-to-0wn-NAS-in-Your-Spare-Time
공식 코드https://github.com/SelfExplainML/SeqUD
공식 코드https://github.com/Smerity/sha-rnn
공식 코드https://github.com/Somedaywilldo/BM-NAS
공식 코드https://github.com/StefanoGiacomelli/Vowel_phonemes_Analysis_and_Classification_by_means_of_OCON_rectifiers_Deep_Learning_Architectures
공식 코드The OCON model: an old but gold solution for distributable supervised classification
https://github.com/Sunshine-Ye/Beta-DARTS
공식 코드$β$-DARTS: Beta-Decay Regularization for Differentiable Architecture Search
https://github.com/TAMU-VITA/AGD
공식 코드AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/TAMU-VITA/AutoGAN
공식 코드AutoGAN: Neural Architecture Search for Generative Adversarial Networks
https://github.com/TAMU-VITA/AutoSpeech
공식 코드AutoSpeech: Neural Architecture Search for Speaker Recognition
https://github.com/TAMU-VITA/FasterSeg
공식 코드FasterSeg: Searching for Faster Real-time Semantic Segmentation
https://github.com/THUMNLab/AutoGL
공식 코드https://github.com/THUMNLab/awesome-auto-graph-learning
공식 코드Automated Graph Machine Learning: Approaches, Libraries, Benchmarks and Directions
https://github.com/TIBHannover/MSVA
공식 코드Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
https://github.com/TengjieZheng/rgpssm
공식 코드https://github.com/TimRoith/BregmanLearning
공식 코드https://github.com/TomVeniat/SANAS
공식 코드Stochastic Adaptive Neural Architecture Search for Keyword Spotting
https://github.com/Tomev/AQMLator
공식 코드https://github.com/UKPLab/emnlp2017-bilstm-cnn-crf
공식 코드Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/UTokyo-ICEPP/multiml
공식 코드https://github.com/UTokyo-ICEPP/multiml_htautau
공식 코드https://github.com/UrbsLab/ExSTraCS_ML_Pipeline_Binary_Notebook
공식 코드https://github.com/UrbsLab/Pancreatic_Cancer_ML_Notebook_Analysis
공식 코드https://github.com/VDIGPKU/OPANAS
공식 코드OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection
https://github.com/VICO-UoE/DatasetCondensation
공식 코드https://github.com/VITA-Group/Sandwich-Batch-Normalization
공식 코드Sandwich Batch Normalization: A Drop-In Replacement for Feature Distribution Heterogeneity
https://github.com/VITA-Group/TENAS
공식 코드Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
https://github.com/VITA-Group/WeakNAS
공식 코드https://github.com/VascoLopes/AutoClassifier
공식 코드Auto-Classifier: A Robust Defect Detector Based on an AutoML Head
https://github.com/VascoLopes/EPE-NAS
공식 코드EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
https://github.com/VascoLopes/EPENAS
공식 코드EPE-NAS: Efficient Performance Estimation Without Training for Neural Architecture Search
https://github.com/VascoLopes/GEA
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.