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 876–900 of 3073 papers

TitleStatusHype
The Best of Both Worlds: Combining Human and Machine Translations for Multilingual Semantic Parsing with Active Learning—0
On the Limitations of Simulating Active Learning—0
DeepEdit: Deep Editable Learning for Interactive Segmentation of 3D Medical ImagesCode2
STREAMLINE: Streaming Active Learning for Realistic Multi-Distributional SettingsCode0
Active Learning in Symbolic Regression with Physical Constraints—0
AbdomenAtlas-8K: Annotating 8,000 CT Volumes for Multi-Organ Segmentation in Three WeeksCode2
On Dataset Transferability in Active Learning for TransformersCode0
Learning to Learn Unlearned Feature for Brain Tumor Segmentation—0
An Active Learning-based Approach for Hosting Capacity Analysis in Distribution Systems—0
Machine-learning-accelerated simulations to enable automatic surface reconstructionCode1
Sequential Experimental Design for Spectral Measurement: Active Learning Using a Parametric Model—0
Active Learning For Contextual Linear Optimization: A Margin-Based Approach—0
Accelerating Batch Active Learning Using Continual Learning Techniques—0
Disentangled Multi-Fidelity Deep Bayesian Active LearningCode1
Actively Discovering New Slots for Task-oriented ConversationCode0
Active Continual Learning: On Balancing Knowledge Retention and Learnability—0
Optimizing Drug Design by Merging Generative AI With Active Learning Frameworks—0
Multi-Domain Learning From Insufficient Annotations—0
Transfer and Active Learning for Dissonance Detection: Addressing the Rare-Class ChallengeCode0
Single-model uncertainty quantification in neural network potentials does not consistently outperform model ensemblesCode0
ADVISE: AI-accelerated Design of Evidence Synthesis for Global Development—0
NewsPanda: Media Monitoring for Timely Conservation ActionCode0
Active Reinforcement Learning for Personalized Stress Monitoring in Everyday Settings—0
Real-time Safety Assessment of Dynamic Systems in Non-stationary Environments: A Review of Methods and TechniquesCode0
Generative Flow Networks for Precise Reward-Oriented Active Learning on Graphs—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