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 1251–1275 of 3073 papers

TitleStatusHype
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Deep Unsupervised Active Learning on Learnable Graphs—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Deep Active Learning for Remote Sensing Object Detection—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning for Video-based Person Re-identification—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Deep Active Learning over the Long Tail—0
Adaptive Active Learning for Image Classification—0
Deep Active Learning Using Barlow Twins—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—0
Bucketized Active Sampling for Learning ACOPF—0
Active Learning for Nonlinear System Identification with Guarantees—0
A Lagrangian Duality Approach to Active Learning—0
Deep Active Learning with Budget Annotation—0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
Confident Coreset for Active Learning in Medical Image Analysis—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