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 1201–1225 of 3073 papers

TitleStatusHype
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Data driven semi-supervised learning—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Data-driven discovery of free-form governing differential equations—0
Data-driven surrogate modelling and benchmarking for process equipment—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Data-Efficient Learning via Minimizing Hyperspherical Energy—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
A Finite-Horizon Approach to Active Level Set Estimation—0
Data-efficient Online Classification with Siamese Networks and Active Learning—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning with Budget Annotation—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Data Shapley Valuation for Efficient Batch Active Learning—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—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