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 526–550 of 3073 papers

TitleStatusHype
Mode Estimation with Partial Feedback—0
ELAD: Explanation-Guided Large Language Models Active Distillation—0
Bayesian Active Learning for Censored Regression—0
Key Patch Proposer: Key Patches Contain Rich InformationCode0
HEAL: Brain-inspired Hyperdimensional Efficient Active Learning—0
Decomposition for Enhancing Attention: Improving LLM-based Text-to-SQL through Workflow ParadigmCode1
Active Preference Optimization for Sample Efficient RLHFCode0
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
Class-Balanced and Reinforced Active Learning on Graphs—0
Self-consistent Validation for Machine Learning Electronic Structure—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
Active Preference Learning for Large Language Models—0
Towards Explainable, Safe Autonomous Driving with Language Embeddings for Novelty Identification and Active Learning: Framework and Experimental Analysis with Real-World Data Sets—0
Video Annotator: A framework for efficiently building video classifiers using vision-language models and active learningCode2
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
ActiveDP: Bridging Active Learning and Data Programming—0
Direct Acquisition Optimization for Low-Budget Active Learning—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Information-Theoretic Active Correlation Clustering—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