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Active Learning
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Active Learning is a paradigm in supervised machine learning which uses fewer training examples to achieve better optimization by iteratively training a predictor, and using the predictor in each iteration to choose the training examples which will increase its chances of finding better configurations and at the same time improving the accuracy of the prediction model
Source: Polystore++: Accelerated Polystore System for Heterogeneous Workloads
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/scorebo/scorebo
Bayesian Active Learning with Fully Bayesian Gaussian Processes
https://github.com/sejong-rcv/OO4AL
https://github.com/seominseok0429/Learning-Loss-for-Active-Learning-Pytorch
https://github.com/seonghwanseo/pharmaconet
TacoGFN: Target-conditioned GFlowNet for Structure-based Drug Design
https://github.com/servicenow/synbols
Synbols: Probing Learning Algorithms with Synthetic Datasets
https://github.com/sghong977/AL-consistency-based
Deep Active Learning with Augmentation-based Consistency Estimation
https://github.com/sleep3r/garrus
https://github.com/songchujun/joeynmt
https://github.com/stanford-futuredata/omg
https://github.com/sumitsk/MOGP-AL
Near-Optimal Active Learning of Multi-Output Gaussian Processes
https://github.com/sunnyyzj/morl-baselines
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement Prioritization
https://github.com/suvarna-kadam/Oct2018_Demo
https://github.com/svdesai/coreset-al
Active Learning for Convolutional Neural Networks: A Core-Set Approach
https://github.com/swanjing/Contextual-Bandits-Active-Learning
https://github.com/swarooprm/DQI
https://github.com/tanyanair/segmentation_uncertainty
https://github.com/tmfs10/ical
https://github.com/tonygsw/Joint-Extraction-of-Entities-and-Relations-Based-on-a-Novel-Tagging-Scheme
https://github.com/tsingqaq/trieste
Trieste: Efficiently Exploring The Depths of Black-box Functions with TensorFlow
https://github.com/tueboesen/Active-Learning
Cost-Effective Active Learning for Deep Image Classification
https://github.com/ungeimer/Etiquetadorv1
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/FLAT-TextTagger
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/FLAT-lite
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/ungeimer/TextTagger-Docker
Active learning in annotating micro-blogs dealing with e-reputation
https://github.com/urbanmobility/SGM_mindspore
https://github.com/verena-hallitschke/pet-ct-annotate
MONAI Label: A framework for AI-assisted Interactive Labeling of 3D Medical Images
https://github.com/wabbajack1/tapd
Progress & Compress: A scalable framework for continual learning
https://github.com/walzimmer/3d-bat
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object Detection
https://github.com/wannature/BoostMIS
https://github.com/webis-de/acl-22
Revisiting Uncertainty-based Query Strategies for Active Learning with Transformers
https://github.com/wenxuanou/SRAAL_Realization
https://github.com/xialeiliu/RankIQA
Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
https://github.com/xxxnhb/few-shot-gnn
https://github.com/yangyucheng000/Paper-4/tree/main/TAA_MindSpore
TAAL: Test-time Augmentation for Active Learning in Medical Image Segmentation
https://github.com/yanxp/ASM
Cost-effective Object Detection: Active Sample Mining with Switchable Selection Criteria
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.