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 226–250 of 3073 papers

TitleStatusHype
Exploiting Contextual Uncertainty of Visual Data for Efficient Training of Deep Models—0
Machine Learning-Accelerated Multi-Objective Design of Fractured Geothermal SystemsCode0
Cost-Aware Query Policies in Active Learning for Efficient Autonomous Robotic Exploration—0
SpiroActive: Active Learning for Efficient Data Acquisition for Spirometry—0
Active Learning for Vision-Language Models—0
DISCERN: Decoding Systematic Errors in Natural Language for Text ClassifiersCode0
Evaluation of uncertainty estimations for Gaussian process regression based machine learning interatomic potentials—0
Annotation Efficiency: Identifying Hard Samples via Blocked Sparse Linear Bandits—0
Efficient Biological Data Acquisition through Inference Set Design—0
Perturbation-based Graph Active Learning for Weakly-Supervised Belief Representation Learning—0
Uncertainty-Error correlations in Evidential Deep Learning models for biomedical segmentation—0
Exploring the Universe with SNAD: Anomaly Detection in Astronomy—0
regAL: Python Package for Active Learning of Regression Problems—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Increasing Interpretability of Neural Networks By Approximating Human Visual Saliency—0
Learning signals defined on graphs with optimal transport and Gaussian process regression—0
Deep Active Learning with Manifold-preserving Trajectory Sampling—0
Coherence-Driven Multimodal Safety Dialogue with Active Learning for Embodied Agents—0
Railway LiDAR semantic segmentation based on intelligent semi-automated data annotation—0
A Simplifying and Learnable Graph Convolutional Attention Network for Unsupervised Knowledge Graphs Alignment—0
An Active Learning Framework for Inclusive Generation by Large Language Models—0
AutoAL: Automated Active Learning with Differentiable Query Strategy SearchCode0
MF-LAL: Drug Compound Generation Using Multi-Fidelity Latent Space Active LearningCode0
Active Learning for Robust and Representative LLM Generation in Safety-Critical Scenarios—0
ALVIN: Active Learning Via INterpolation—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