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 531–540 of 3073 papers

TitleStatusHype
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling—0
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Active Preference Optimization for Sample Efficient RLHFCode0
Self-consistent Validation for Machine Learning Electronic Structure—0
Class-Balanced and Reinforced Active Learning on Graphs—0
Role-Playing Simulation Games using ChatGPT—0
Reinforcement Learning from Human Feedback with Active Queries—0
Transductive Active Learning: Theory and ApplicationsCode2
Active Few-Shot Fine-Tuning—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