SOTAVerified

Active Learning

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

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2826–2850 of 3073 papers

TitleStatusHype
Autonomous synthesis of metastable materials—0
Autonomy and Reliability of Continuous Active Learning for Technology-Assisted Review—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
A Word-and-Paradigm Workflow for Fieldwork Annotation—0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
Balancing Accuracy, Calibration, and Efficiency in Active Learning with Vision Transformers Under Label Noise—0
BAOD: Budget-Aware Object Detection—0
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Batch Active Learning from the Perspective of Sparse Approximation—0
Batch Active Learning of Reward Functions from Human Preferences—0
Batch Active Learning via Coordinated Matching—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Batch versus Sequential Active Learning for Recommender Systems—0
BayesFormer: Transformer with Uncertainty Estimation—0
Bayesian Active Edge Evaluation on Expensive Graphs—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Bayesian Active Learning for Censored Regression—0
Bayesian active learning for choice models with deep Gaussian processes—0
Efficient Sampling-Based Bayesian Active Learning for synaptic characterization—0
Bayesian Active Learning for Discrete Latent Variable Models—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TypiClustAccuracy93.2—Unverified
2PT4ALAccuracy93.1—Unverified
3Learning lossAccuracy91.01—Unverified
4CoreGCNAccuracy90.7—Unverified
5Core-setAccuracy89.92—Unverified
6Random Baseline (Resnet18)Accuracy88.45—Unverified
7Random Baseline (VGG16)Accuracy85.09—Unverified