https://github.com/jgonsior/ImitAL
공식 코드ImitAL: Learning Active Learning Strategies from Synthetic Data
Papers with Code 아카이브
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/jgonsior/btw-softmax-clipping
공식 코드https://github.com/jgonsior/olympic-games-of-active-learning
공식 코드Survey of Active Learning Hyperparameters: Insights from a Large-Scale Experimental Grid
https://github.com/jiangnanhugo/apps-ode
공식 코드Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching
https://github.com/jiayichen815/feal
공식 코드https://github.com/jiezou0806/SBSTAR
공식 코드https://github.com/jifanz/GALAXY
공식 코드https://github.com/jifanz/tailor
공식 코드Algorithm Selection for Deep Active Learning with Imbalanced Datasets
https://github.com/jilljenn/vae
공식 코드Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender Systems
https://github.com/jinchaogjc/esa
공식 코드ESA: Annotation-Efficient Active Learning for Semantic Segmentation
https://github.com/jjzha/cal
공식 코드https://github.com/jlko/active-surrogate-estimators
공식 코드Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation
https://github.com/jlko/active-testing
공식 코드https://github.com/jmandivarapu1/Deep-Active-Learning-via-Open-Set-Recognition
공식 코드https://github.com/jnzju/coti
공식 코드Controllable Textual Inversion for Personalized Text-to-Image Generation
https://github.com/joerntebbe/safetybounds4gpinal
공식 코드Efficiently Computable Safety Bounds for Gaussian Processes in Active Learning
https://github.com/joeynmt/joeynmt
공식 코드https://github.com/johnmartinsson/adaptive-change-point-detection
공식 코드From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active Learning
https://github.com/johnsk95/pt4al
공식 코드PT4AL: Using Self-Supervised Pretext Tasks for Active Learning
https://github.com/joisino/seafaring
공식 코드https://github.com/jonaselgammal/gpry
공식 코드Fast and robust Bayesian Inference using Gaussian Processes with GPry
https://github.com/jonhue/activeft
공식 코드Efficiently Learning at Test-Time: Active Fine-Tuning of LLMs
https://github.com/jonzarecki/textual-membership-queries
공식 코드https://github.com/jonzarecki/textual-mqs
공식 코드https://github.com/josipjukic/adapter-al
공식 코드Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource Settings
https://github.com/jpfolch/catechol_solvent_selection
공식 코드The Catechol Benchmark: Time-series Solvent Selection Data for Few-shot Machine Learning
https://github.com/juangamella/abcd
공식 코드Active Invariant Causal Prediction: Experiment Selection through Stability
https://github.com/juangamella/aicp
공식 코드Active Invariant Causal Prediction: Experiment Selection through Stability
https://github.com/juliakreutzer/joeynmt
공식 코드https://github.com/julietljy/bal
공식 코드https://github.com/justherozen/freeal
공식 코드FreeAL: Towards Human-Free Active Learning in the Era of Large Language Models
https://github.com/jwcalder/mstar-active-learning
공식 코드Graph-based Active Learning for Semi-supervised Classification of SAR Data
https://github.com/kadarsh22/disentanglement_based_active_learning
공식 코드https://github.com/kaist-dmlab/mqnet
공식 코드Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
https://github.com/kaist-dmlab/pcb
공식 코드https://github.com/kaist-dmlab/taufe
공식 코드Prioritizing Informative Features and Examples for Deep Learning from Noisy Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.