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 1251–1300 of 3073 papers

TitleStatusHype
Deep Active Learning for Efficient Training of a LiDAR 3D Object Detector—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Diameter-based Interactive Structure Discovery—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Deep Active Learning for Remote Sensing Object Detection—0
Does Deep Active Learning Work in the Wild?—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Deep Active Learning for Text Classification with Diverse Interpretations—0
Deep Active Learning for Video-based Person Re-identification—0
Dirichlet Active Learning—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Deep Active Learning over the Long Tail—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—0
Bucketized Active Sampling for Learning ACOPF—0
Adaptive Active Learning for Image Classification—0
A Lagrangian Duality Approach to Active Learning—0
Deep Active Learning with Budget Annotation—0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification—0
Deep Active Learning with Manifold-preserving Trajectory Sampling—0
Active Learning for Nonlinear System Identification with Guarantees—0
Deep Active Learning with Noisy Oracle in Object Detection—0
Confident Coreset for Active Learning in Medical Image Analysis—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Active and Semi-Supervised Learning in ASR: Benefits on the Acoustic and Language Models—0
Confidence Estimation for Object Detection in Document Images—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
Deep Bayesian Active Learning for Multiple Correct Outputs—0
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Deep Bayesian Active Learning-to-Rank with Relative Annotation for Estimation of Ulcerative Colitis Severity—0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
Confidence Decision Trees via Online and Active Learning for Streaming (BIG) Data—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Algorithmic Performance-Accuracy Trade-off in 3D Vision Applications Using HyperMapper—0
Adaptive Active Hypothesis Testing under Limited Information—0
Detecting Repeating Objects using Patch Correlation Analysis—0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
Deterministic Langevin Unconstrained Optimization with Normalizing Flows—0
Deep Ensemble Bayesian Active Learning : Addressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—0
Deep imitation learning for molecular inverse problems—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—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