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 401–450 of 3073 papers

TitleStatusHype
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—0
Fairness-Driven LLM-based Causal Discovery with Active Learning and Dynamic Scoring—0
Uncertainty Meets Diversity: A Comprehensive Active Learning Framework for Indoor 3D Object Detection—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
Efficient Data Selection for Training Genomic Perturbation Models—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community—0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions—0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Unique Rashomon Sets for Robust Active LearningCode0
Instance-wise Supervision-level Optimization in Active LearningCode0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
Dependency-aware Maximum Likelihood Estimation for Active Learning—0
Near-Polynomially Competitive Active Logistic RegressionCode0
Enhancing SAM with Efficient Prompting and Preference Optimization for Semi-supervised Medical Image Segmentation—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery—0
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