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 426–450 of 3073 papers

TitleStatusHype
Active Learning for Direct Preference Optimization—0
Architectural and Inferential Inductive Biases For Exchangeable Sequence ModelingCode0
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment—0
DUAL: Diversity and Uncertainty Active Learning for Text SummarizationCode0
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—0
Rethinking Epistemic and Aleatoric Uncertainty for Active Open-Set Annotation: An Energy-Based ApproachCode0
Learning atomic forces from uncertainty-calibrated adversarial attacksCode0
Active Learning for Conditional Inverse Design with Crystal Generation and Foundation Atomic Models—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Active Learning Classification from a Signal Separation Perspective—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design—0
From Selection to Generation: A Survey of LLM-based Active Learning—0
Co-designing Large Language Model Tools for Project-Based Learning with K12 Educators—0
Enhancing RAG with Active Learning on Conversation Records: Reject Incapables and Answer Capables—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course—0
Towards a Foundation Model for Physics-Informed Neural Networks: Multi-PDE Learning with Active Sampling—0
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories—0
Probabilistic Artificial Intelligence—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
AL-PINN: Active Learning-Driven Physics-Informed Neural Networks for Efficient Sample Selection in Solving Partial Differential Equations—0
Mining Unstructured Medical Texts With Conformal Active Learning—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