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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/razvancaramalau/al_bhpe
공식 코드https://github.com/razvancaramalau/fedalv
공식 코드https://github.com/razvancaramalau/mobyv2al
공식 코드MoBYv2AL: Self-supervised Active Learning for Image Classification
https://github.com/rcrupiisp/siamesedisambiguation
공식 코드https://github.com/rickymaulanafajri/fal-cur
공식 코드FAL-CUR: Fair Active Learning using Uncertainty and Representativeness on Fair Clustering
https://github.com/rickymaulanafajri/spa
공식 코드A Structural-Clustering Based Active Learning for Graph Neural Networks
https://github.com/ridiculouz/llmaaa
공식 코드https://github.com/rio98/hybrid-representation-enhanced-bayesian-active-learning
공식 코드https://github.com/rlphilli/InterpretableActiveLearning
공식 코드https://github.com/rose-stl-lab/interactive-neural-process
공식 코드Deep Bayesian Active Learning for Accelerating Stochastic Simulation
https://github.com/rose-stl-lab/mf-lal
공식 코드MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active Learning
https://github.com/rose-stl-lab/multi-fidelity-deep-active-learning
공식 코드https://github.com/rothkopflab/nioc-neurips
공식 코드https://github.com/rotmanguy/mtal
공식 코드Multi-task Active Learning for Pre-trained Transformer-based Models
https://github.com/rowanlibr/sophisticated-learning
공식 코드Planning to Learn: A Novel Algorithm for Active Learning during Model-Based Planning
https://github.com/rpinsler/active-bayesian-coresets
공식 코드Bayesian Batch Active Learning as Sparse Subset Approximation
https://github.com/rrmenon10/DISCERN
공식 코드DISCERN: Decoding Systematic Errors in Natural Language for Text Classifiers
https://github.com/ruidaniel/rbaca
공식 코드Continual Deep Active Learning for Medical Imaging: Replay-Base Architecture for Context Adaptation
https://github.com/ruijiang81/AGB
공식 코드https://github.com/ruiyum/active-openset-neat
공식 코드https://github.com/ruiyum/stone
공식 코드STONE: A Submodular Optimization Framework for Active 3D Object Detection
https://github.com/rz-zhang/SeqMix
공식 코드SeqMix: Augmenting Active Sequence Labeling via Sequence Mixup
https://github.com/rzayanov/irl-teaching-limited-feedback
공식 코드Interactively Teaching an Inverse Reinforcement Learner with Limited Feedback
https://github.com/s-a-malik/batchgfn
공식 코드BatchGFN: Generative Flow Networks for Batch Active Learning
https://github.com/sabjoslo/active-learning-bias
공식 코드Characterizing the robustness of Bayesian adaptive experimental designs to active learning bias
https://github.com/sachalevy/active-keyword-selection
공식 코드Active Keyword Selection to Track Evolving Topics on Twitter
https://github.com/sail-sg/activeprm
공식 코드https://github.com/samgriesemer/seqinf
공식 코드Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based Inference
https://github.com/samottaghi/adversarial-representation-active-learning
공식 코드https://github.com/sampolk/d-vic
공식 코드Active Diffusion and VCA-Assisted Image Segmentation of Hyperspectral Images
https://github.com/samsja/finding-a-needle-code
공식 코드https://github.com/samsungsailmontreal/stgg-al
공식 코드Generating $π$-Functional Molecules Using STGG+ with Active Learning
https://github.com/samuelstanton/conformal-bayesopt
공식 코드https://github.com/sangwoomo/gold
공식 코드https://github.com/sanketx/al-foundation-models
공식 코드Revisiting Active Learning in the Era of Vision Foundation Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.