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 101–125 of 3073 papers

TitleStatusHype
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
Instance-wise Supervision-level Optimization in Active LearningCode0
Unique Rashomon Sets for Robust Active LearningCode0
NeuroADDA: Active Discriminative Domain Adaptation in Connectomic—0
Near-Polynomially Competitive Active Logistic RegressionCode0
Dependency-aware Maximum Likelihood Estimation for Active Learning—0
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
Architectural and Inferential Inductive Biases For Exchangeable Sequence ModelingCode0
Active Learning for Direct Preference Optimization—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
Applying LLMs to Active Learning: Towards Cost-Efficient Cross-Task Text Classification without Manually Labeled Data—0
Distributionally Robust Active Learning for Gaussian Process Regression—0
Active Learning Classification from a Signal Separation Perspective—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—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