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 101–150 of 3073 papers

TitleStatusHype
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions—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
CREStE: Scalable Mapless Navigation with Internet Scale Priors and Counterfactual Guidance—0
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
LAPD: Langevin-Assisted Bayesian Active Learning for Physical Discovery—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using Active Learning—0
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment—0
Active Learning for Direct Preference Optimization—0
Architectural and Inferential Inductive Biases For Exchangeable Sequence ModelingCode0
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
Generating π-Functional Molecules Using STGG+ with Active LearningCode1
From Selection to Generation: A Survey of LLM-based Active Learning—0
Physics-Informed Gaussian Process Classification for Constraint-Aware Alloy Design—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
ActiveSSF: An Active-Learning-Guided Self-Supervised Framework for Long-Tailed Megakaryocyte Classification—0
Multifidelity Simulation-based Inference for Computationally Expensive Simulators—0
Towards a Foundation Model for Physics-Informed Neural Networks: Multi-PDE Learning with Active Sampling—0
Educating a Responsible AI Workforce: Piloting a Curricular Module on AI Policy in a Graduate Machine Learning Course—0
A physics-based data-driven model for CO_2 gas diffusion electrodes to drive automated laboratories—0
Probabilistic Artificial IntelligenceCode0
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
DenseReviewer: A Screening Prioritisation Tool for Systematic Review based on Dense RetrievalCode0
Mining Unstructured Medical Texts With Conformal Active Learning—0
Omni-Mol: Exploring Universal Convergent Space for Omni-Molecular Tasks—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Integrating Semi-Supervised and Active Learning for Semantic Segmentation—0
Reducing Aleatoric and Epistemic Uncertainty through Multi-modal Data AcquisitionCode0
Amortized Safe Active Learning for Real-Time Data Acquisition: Pretrained Neural Policies from Simulated Nonparametric Functions—0
Breaking the SSL-AL Barrier: A Synergistic Semi-Supervised Active Learning Framework for 3D Object Detection—0
Gaussian-Process-based Adaptive Tracking Control with Dynamic Active Learning for Autonomous Ground Vehicles—0
Hybrid Interpretable Deep Learning Framework for Skin Cancer Diagnosis: Integrating Radial Basis Function Networks with Explainable AI—0
Active Learning for Continual Learning: Keeping the Past Alive in the Present—0
Efficient Auto-Labeling of Large-Scale Poultry Datasets (ALPD) Using Semi-Supervised Models, Active Learning, and Prompt-then-Detect Approach—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