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Active Learning

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하위 태스크: 1
평가표: 1
보존된 평가 기록: 7

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/ShengdingHu/GraphPolicyNetworkActiveLearning

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Graph Policy Network for Transferable Active Learning on Graphs

https://github.com/SineZHAN/deepALplus

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A Comparative Survey of Deep Active Learning

https://github.com/SkafteNicki/john

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Reliable training and estimation of variance networks

https://github.com/Souvic/aspect-based-sentiment-analysis-of-scientific-reviews-jcdl

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Aspect-based Sentiment Analysis of Scientific Reviews

https://github.com/Stanford-ILIAD/DPP-Batch-Active-Learning

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Batch Active Learning Using Determinantal Point Processes

https://github.com/Stanford-ILIAD/batch-active-preference-based-learning

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Batch Active Preference-Based Learning of Reward Functions

https://github.com/SteffenCzolbe/probabilistic_segmentation

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Is segmentation uncertainty useful?

https://github.com/SvenL13/LocalValidity

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Quantifying Local Model Validity using Active Learning

https://github.com/System-T/PARTNER

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Learning Structured Representations of Entity Names using Active Learning and Weak Supervision

https://github.com/ThuongTNguyen/ALchemist

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On the Fragility of Active Learners for Text Classification

https://github.com/TonyLianLong/UnsupervisedSelectiveLabeling

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Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

https://github.com/UKPLab/acl2020-empowering-active-learning

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Empowering Active Learning to Jointly Optimize System and User Demands

https://github.com/UKPLab/arxiv2018-bayesian-ensembles

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A Bayesian Approach for Sequence Tagging with Crowds

https://github.com/UKPLab/emnlp2018-april

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APRIL: Interactively Learning to Summarise by Combining Active Preference Learning and Reinforcement Learning

https://github.com/UKPLab/irj-neural-april

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Preference-based Interactive Multi-Document Summarisation

https://github.com/UKPLab/tacl2018-preference-convincing

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Finding Convincing Arguments Using Scalable Bayesian Preference Learning

https://github.com/VRM1/ActiveLearning

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Efficacy of Bayesian Neural Networks in Active Learning

https://github.com/Vanint/OA3

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Online Adaptive Asymmetric Active Learning with Limited Budgets

https://github.com/WILAB-HIT/Resources

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Building a comprehensive syntactic and semantic corpus of Chinese clinical texts

https://github.com/WenboRen/Topk-Ranking-from-Pairwise-Comparisons

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The Sample Complexity of Best-$k$ Items Selection from Pairwise Comparisons

https://github.com/XavierOwen/ALC-AEGP

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Active Learning for Manifold Gaussian Process Regression

https://github.com/Xtra-Computing/thundersvm

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Sampling Bias in Deep Active Classification: An Empirical Study

https://github.com/YazhouTUD/MVAL

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A Variance Maximization Criterion for Active Learning

https://github.com/YazhouTUD/single_shot_AL

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Single Shot Active Learning using Pseudo Annotators

https://github.com/YeolJ00/faster-rcnn-pytorch

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Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound Images

https://github.com/YilunZhou/optimal-active-learning

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Towards Understanding the Behaviors of Optimal Deep Active Learning Algorithms

https://github.com/YoungTimmy/SAG

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Mitigating Semantic Confusion from Hostile Neighborhood for Graph Active Learning

https://github.com/ZiTingW/asvp

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Feature Alignment: Rethinking Efficient Active Learning via Proxy in the Context of Pre-trained Models

https://github.com/aair-lab/qace

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Autonomous Capability Assessment of Sequential Decision-Making Systems in Stochastic Settings (Extended Version)

https://github.com/aaltoml/sequential-gp

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Memory-Based Dual Gaussian Processes for Sequential Learning

https://github.com/aaltopml/human-in-the-loop-predictive-maintenance

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Human-in-the-Loop Large-Scale Predictive Maintenance of Workstations

https://github.com/abhijeetptl5/historoi

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Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop Training

https://github.com/abiy-tasissa/VAE-LAND

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Deep Diffusion Processes for Active Learning of Hyperspectral Images

https://github.com/ablancha/gpsearch

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Output-Weighted Optimal Sampling for Bayesian Experimental Design and Uncertainty Quantification

https://github.com/acerbilab/vsbq

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Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature

https://github.com/acl21/init-pools-dal

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On Initial Pools for Deep Active Learning

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