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 201–250 of 3073 papers

TitleStatusHype
ORIS: Online Active Learning Using Reinforcement Learning-based Inclusive Sampling for Robust Streaming Analytics System—0
Active partitioning: inverting the paradigm of active learning—0
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
Maximally Separated Active Learning—0
A Review of Bayesian Uncertainty Quantification in Deep Probabilistic Image Segmentation—0
Integrating Deep Metric Learning with Coreset for Active Learning in 3D SegmentationCode0
Benchmarking Active Learning for NILM—0
Influence functions and regularity tangents for efficient active learning—0
Active Learning-Based Optimization of Hydroelectric Turbine Startup to Minimize Fatigue Damage—0
LPLgrad: Optimizing Active Learning Through Gradient Norm Sample Selection and Auxiliary Model TrainingCode0
Integration of Active Learning and MCMC Sampling for Efficient Bayesian Calibration of Mechanical Properties—0
Stream-Based Active Learning for Process Monitoring—0
Active learning for efficient discovery of optimal gene combinations in the combinatorial perturbation spaceCode0
Progressive Generalization Risk Reduction for Data-Efficient Causal Effect EstimationCode0
MolParser: End-to-end Visual Recognition of Molecule Structures in the Wild—0
Targeting Negative Flips in Active Learning using Validation SetsCode0
Learning Quantitative Automata Modulo Theories—0
Deep Active Learning in the Open World—0
GCI-ViTAL: Gradual Confidence Improvement with Vision Transformers for Active Learning on Label Noise—0
FisherMask: Enhancing Neural Network Labeling Efficiency in Image Classification Using Fisher InformationCode0
Performance-Guided LLM Knowledge Distillation for Efficient Text Classification at Scale—0
Hands-On Tutorial: Labeling with LLM and Human-in-the-Loop—0
Constrained Multi-objective Bayesian Optimization through Optimistic Constraints EstimationCode0
An information-matching approach to optimal experimental design and active learning—0
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