Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 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/bhanML/Co-teaching
Searching to Exploit Memorization Effect in Learning from Corrupted Labels
https://github.com/bibliotecadebabel/EvAI
Traditional and accelerated gradient descent for neural architecture search
https://github.com/billchan226/poar-srl-4-robot
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
https://github.com/blessengeorge/compare_gan
https://github.com/blupblupblup/near-ood-doppler-signatures
MedMNIST Classification Decathlon: A Lightweight AutoML Benchmark for Medical Image Analysis
https://github.com/bonniesjli/PPO-Reacher_UnityML
https://github.com/bonniesjli/PPO_Reacher
https://github.com/bowenbaker/metaqnn
Accelerating Neural Architecture Search using Performance Prediction
https://github.com/brethvoice/optuna_demo_MNIST
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/buiquangmanhhp1999/age_gender_estimation
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/c-bata/benchmark-warm-starting-cmaes
https://github.com/c-bata/goptuna
Practical Bayesian Optimization of Machine Learning Algorithms
https://github.com/calvinmccarter/kditransform
TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second
https://github.com/canturan10/satellighte
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/captaindario/dakanji-single-kanji-recognition
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/carolchenyx/MobilenetXT-tensorflow
Rethinking Bottleneck Structure for Efficient Mobile Network Design
https://github.com/carpedm20/ENAS-pytorch
https://github.com/catniplab/xfads
Enabling hyperparameter optimization in sequential autoencoders for spiking neural data
https://github.com/cgebbe/kaggle_pku-autonomous-driving
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/chainer/chainerrl
https://github.com/chandlerbang/gcond
GC4NC: A Benchmark Framework for Graph Condensation on Node Classification with New Insights
https://github.com/chariff/GPro
https://github.com/chenfengxu714/image2point
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/chenyaofo/ASE-NAS
Automated Dominative Subspace Mining for Efficient Neural Architecture Search
https://github.com/chris-boson/fashion_mnist
https://github.com/chrisqqq123/FA-Dist-EfficientNet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/christiansafka/img2vec
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/chrisway613/MixConv
https://github.com/chumingqian/Model_Compression_For_YOLOV3-V4
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/cipher982/ppo-exploration
https://github.com/civisanalytics/civisml-extensions
Hyperband: A Novel Bandit-Based Approach to Hyperparameter Optimization
https://github.com/ckyrkou/EmergencyNet
https://github.com/claesenm/optunity
https://github.com/clovaai/rexnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/clwainwright/proximal_policy_optimization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.