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 2451–2475 of 3073 papers

TitleStatusHype
Active learning for binary classification with variable selection—0
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Limitations of Assessing Active Learning Performance at Runtime—0
Stopping Active Learning based on Predicted Change of F Measure for Text Classification—0
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification—0
What does the free energy principle tell us about the brain?—0
Cascade Submodular Maximization: Question Selection and Sequencing in Online Personality Quiz—0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
Diverse mini-batch Active Learning—0
ALiPy: Active Learning in PythonCode0
Active Learning for One-Class Classification Using Two One-Class Classifiers—0
Guess What's on my Screen? Clustering Smartphone Screenshots with Active Learning—0
Risk-Aware Active Inverse Reinforcement LearningCode0
Efforts estimation of doctors annotating medical image—0
Learning From Less Data: A Unified Data Subset Selection and Active Learning Framework for Computer Vision—0
Active Learning with TensorBoard Projector—0
An Active Learning Framework for Efficient Robust Policy Search—0
Weakly Supervised Active Learning with Cluster Annotation—0
A General Approach to Domain Adaptation with Applications in Astronomy—0
Active Learning and CSI Acquisition for mmWave Initial Alignment—0
Machine Learning for Molecular Dynamics on Long Timescales—0
Molecular Dynamics with Neural-Network Potentials—0
Deep Active Learning for Video-based Person Re-identification—0
SMART: An Open Source Data Labeling Platform for Supervised Learning—0
Improving Model-Based Control and Active Exploration with Reconstruction Uncertainty Optimization—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