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 2226–2250 of 3073 papers

TitleStatusHype
ViewAL: Active Learning with Viewpoint Entropy for Semantic SegmentationCode0
A User Study of Perceived Carbon Footprint—0
Actively Learning Gaussian Process DynamicsCode0
Active Learning for Deep Detection Neural NetworksCode0
Deep Active Learning: Unified and Principled Method for Query and TrainingCode0
Investigating Active Learning and Meta-Learning for Iterative Peptide Design—0
Rethinking deep active learning: Using unlabeled data at model trainingCode0
Bias-Aware Heapified Policy for Active Learning—0
Online Adaptive Asymmetric Active Learning with Limited BudgetsCode0
The Effectiveness of Variational Autoencoders for Active Learning—0
Using Error Decay Prediction to Overcome Practical Issues of Deep Active Learning for Named Entity Recognition—0
Active learning in the geometric block model—0
Coincidence, Categorization, and Consolidation: Learning to Recognize Sounds with Minimal Supervision—0
Cost-efficient segmentation of electron microscopy images using active learning—0
Incentive Compatible Active Learning—0
Context-aware Active Multi-Step Reinforcement Learning—0
An Overview of Data-Importance Aware Radio Resource Management for Edge Machine Learning—0
Bayesian Active Learning for Structured Output Design—0
Adaptivity in Adaptive Submodularity—0
Interactive Refinement of Cross-Lingual Word EmbeddingsCode0
Subspace Clustering with Active Learning—0
Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning—0
Char-RNN and Active Learning for Hashtag Segmentation—0
Active Learning for Black-Box Adversarial Attacks in EEG-Based Brain-Computer Interfaces—0
Assessing the Frontier: Active Learning, Model Accuracy, and Multi-objective Materials Discovery and 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