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 476–500 of 3073 papers

TitleStatusHype
STAYKATE: Hybrid In-Context Example Selection Combining Representativeness Sampling and Retrieval-based Approach -- A Case Study on Science Domains—0
Image Classification with Deep Reinforcement Active Learning—0
TSceneJAL: Joint Active Learning of Traffic Scenes for 3D Object DetectionCode0
Uncertainty Quantification in Continual Open-World Learning—0
Function Space Diversity for Uncertainty Prediction via Repulsive Last-Layer Ensembles—0
GALOT: Generative Active Learning via Optimizable Zero-shot Text-to-image Generation—0
Active Reinforcement Learning Strategies for Offline Policy Improvement—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
Active Large Language Model-based Knowledge Distillation for Session-based Recommendation—0
An Active Parameter Learning Approach to The Identification of Safe Regions—0
Safe Active Learning for Gaussian Differential Equations—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
The Cost of Replicability in Active Learning—0
Enhancing Modality Representation and Alignment for Multimodal Cold-start Active Learning—0
How to select slices for annotation to train best-performing deep learning segmentation models for cross-sectional medical images?—0
Improving Active Learning with a Bayesian Representation of Epistemic Uncertainty—0
MAPLE: A Framework for Active Preference Learning Guided by Large Language Models—0
Label Distribution Learning using the Squared Neural Family on the Probability Simplex—0
Quantifying the Prediction Uncertainty of Machine Learning Models for Individual Data—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Class Balance Matters to Active Class-Incremental LearningCode0
Materials-Discovery Workflows Guided by Symbolic Regression: Identifying Acid-Stable Oxides for ElectrocatalysisCode0
Active Sequential Posterior Estimation for Sample-Efficient Simulation-Based InferenceCode0
Multi-Layer Privacy-Preserving Record Linkage with Clerical Review based on gradual information disclosure—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