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 401–425 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
Preference Elicitation for Multi-objective Combinatorial Optimization with Active Learning and Maximum Likelihood Estimation—0
Active Learning from Scene Embeddings for End-to-End Autonomous Driving—0
Towards Regulatory-Confirmed Adaptive Clinical Trials: Machine Learning Opportunities and SolutionsCode0
Have LLMs Made Active Learning Obsolete? Surveying the NLP Community—0
Active Learning Inspired ControlNet Guidance for Augmenting Semantic Segmentation Datasets—0
QuickDraw: Fast Visualization, Analysis and Active Learning for Medical Image SegmentationCode0
ADROIT: A Self-Supervised Framework for Learning Robust Representations for Active Learning—0
Learning Nash Equilibrial Hamiltonian for Two-Player Collision-Avoiding Interactions—0
Generative method for aerodynamic optimization based on classifier-free guided denoising diffusion probabilistic model—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 operator learning with predictive uncertainty quantification for partial differential equations—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—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
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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