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 2126–2150 of 3073 papers

TitleStatusHype
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm—0
Using Serious Games to Correct French Dictations: Proposal for a New Unity3D/NooJ Connector—0
VaB-AL: Incorporating Class Imbalance and Difficulty with Variational Bayes for Active Learning—0
Value of Information Analysis via Active Learning and Knowledge Sharing in Error-Controlled Adaptive Kriging—0
Value Preferences Estimation and Disambiguation in Hybrid Participatory Systems—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
Variational Adaptive-Newton Method for Explorative Learning—0
Vibration-based Uncertainty Estimation for Learning from Limited Supervision—0
Video Annotation and Tracking with Active Learning—0
VideoCoT: A Video Chain-of-Thought Dataset with Active Annotation Tool—0
Virtual Reality via Object Pose Estimation and Active Learning: Realizing Telepresence Robots with Aerial Manipulation Capabilities—0
Visual Causal Feature Learning—0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis—0
Visual Supervision in Bootstrapped Information Extraction—0
Voice for the Voiceless: Active Sampling to Detect Comments Supporting the Rohingyas—0
VOILA: An Optimised Dialogue System for Interactively Learning Visually-Grounded Word Meanings (Demonstration System)—0
Warm Start Active Learning with Proxy Labels \& Selection via Semi-Supervised Fine-Tuning—0
Weakly Supervised Active Learning with Cluster Annotation—0
Weather and Light Level Classification for Autonomous Driving: Dataset, Baseline and Active Learning—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification—0
Weighted Ensembles for Active Learning with Adaptivity—0
Physics-informed active learning with simultaneous weak-form latent space dynamics identification—0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper—0
What can be learned from satisfaction assessments?—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