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 201–225 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
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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