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 551–575 of 3073 papers

TitleStatusHype
AutoAL: Automated Active Learning with Differentiable Query Strategy SearchCode0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active LearningCode0
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios—0
ALVIN: Active Learning Via INterpolation—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
MelissaDL x Breed: Towards Data-Efficient On-line Supervised Training of Multi-parametric Surrogates with Active Learning—0
Interactive Event Sifting using Bayesian Graph Neural Networks—0
Improved detection of discarded fish species through BoxAL active learningCode0
Language Model-Driven Data Pruning Enables Efficient Active Learning—0
STONE: A Submodular Optimization Framework for Active 3D Object DetectionCode0
Dual Active Learning for Reinforcement Learning from Human Feedback—0
Active Learning of Deep Neural Networks via Gradient-Free Cutting Planes—0
GPT-4o as the Gold Standard: A Scalable and General Purpose Approach to Filter Language Model Pretraining Data—0
Structural-Entropy-Based Sample Selection for Efficient and Effective Learning—0
Provably Accurate Shapley Value Estimation via Leverage Score Sampling—0
Differentially Private Active Learning: Balancing Effective Data Selection and PrivacyCode0
Efficient Quality Control of Whole Slide Pathology Images with Human-in-the-loop TrainingCode0
Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework—0
A3: Active Adversarial Alignment for Source-Free Domain AdaptationCode0
Towards an active-learning approach to resource allocation for population-based damage prognosis—0
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation—0
Find Rhinos without Finding Rhinos: Active Learning with Multimodal Imagery of South African Rhino HabitatsCode0
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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