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 801–825 of 3073 papers

TitleStatusHype
Towards accelerating physical discovery via non-interactive and interactive multi-fidelity Bayesian Optimization: Current challenges and future opportunitiesCode0
Fairness Without Harm: An Influence-Guided Active Sampling ApproachCode0
Mode Estimation with Partial Feedback—0
Integrating Active Learning in Causal Inference with Interference: A Novel Approach in Online Experiments—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
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Active Preference Optimization for Sample Efficient RLHFCode0
MFBind: a Multi-Fidelity Approach for Evaluating Drug Compounds in Practical Generative Modeling—0
Self-consistent Validation for Machine Learning Electronic Structure—0
Class-Balanced and Reinforced Active Learning on Graphs—0
Reinforcement Learning from Human Feedback with Active Queries—0
Role-Playing Simulation Games using ChatGPT—0
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
Safe Active Learning for Time-Series Modeling with Gaussian Processes—0
Direct Acquisition Optimization for Low-Budget Active Learning—0
ActiveDP: Bridging Active Learning and Data Programming—0
An Artificial Intelligence (AI) workflow for catalyst design and optimization—0
Empowering Language Models with Active Inquiry for Deeper Understanding—0
Enhanced sampling of robust molecular datasets with uncertainty-based collective variables—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