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/open-edge-platform/training_extensions
https://github.com/open-mmlab/mmclassification
https://github.com/open-mmlab/mmdetection
NAS-FPN: Learning Scalable Feature Pyramid Architecture for Object Detection
https://github.com/open-mmlab/mmpose
https://github.com/open-mmlab/mmrazor
https://github.com/openml-labs/gama
https://github.com/openpsi-projects/srl
https://github.com/optuna/kurobako
https://github.com/optuna/optuna
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/optuna/optuna-integration
Optuna: A Next-generation Hyperparameter Optimization Framework
https://github.com/osmr/imgclsmob
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/park-cheol/ASR-Conformer
https://github.com/pdefraene/cgp_cnn_predictors
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/pengboxiangshang/mobilenetv3_pytorch
https://github.com/peteryuX/pcdarts-tf2
PC-DARTS: Partial Channel Connections for Memory-Efficient Architecture Search
https://github.com/pfistfl/autoxgboostMC
https://github.com/phossen/autokeras
https://github.com/phykn/film-defect-detection
https://github.com/pikkaay/efficientnet_gpu
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/pipilurj/BONAS
https://github.com/pjlab-adg/openpcseg
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/pjlab-adg/pcseg
Searching Efficient 3D Architectures with Sparse Point-Voxel Convolution
https://github.com/pnnl/torchntk
Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
https://github.com/polarizationpruning/PolarizationPruning
Learning Efficient Convolutional Networks through Network Slimming
https://github.com/pprp/ofa-cifar
Once-for-All: Train One Network and Specialize it for Efficient Deployment
https://github.com/prakhargoyal106/MelanomaClassification
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/prolearner/hyper-representation
Far-HO: A Bilevel Programming Package for Hyperparameter Optimization and Meta-Learning
https://github.com/pwc-1/Paper-9/tree/main/6/GraphPAS
GraphPAS: Parallel Architecture Search for Graph Neural Networks
https://github.com/py-why/causaltune
Out-of-sample scoring and automatic selection of causal estimators
https://github.com/pytorch/rl/tree/main/examples/ppo
https://github.com/pytorch/vision
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/qingyun-wu/autogen-eval
Cost-Effective Hyperparameter Optimization for Large Language Model Generation Inference
https://github.com/qubvel/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/rajneeshaggarwal/google-efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/ravi02512/efficientdet-keras
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.