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 1901–1950 of 3073 papers

TitleStatusHype
DSAL: Deeply Supervised Active Learning from Strong and Weak Labelers for Biomedical Image SegmentationCode0
Active Hybrid Classification—0
Active Learning for Sequence Tagging with Deep Pre-trained Models and Bayesian Uncertainty Estimates—0
Autonomous synthesis of metastable materials—0
Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach—0
Diverse Complexity Measures for Dataset Curation in Self-driving—0
Quality meets Diversity: A Model-Agnostic Framework for Computerized Adaptive Testing—0
PowerEvaluationBALD: Efficient Evaluation-Oriented Deep (Bayesian) Active Learning with Stochastic Acquisition Functions—0
Improved active output selection strategy for noisy environments—0
Deep Diffusion Processes for Active Learning of Hyperspectral ImagesCode0
Application of an automated machine learning-genetic algorithm (AutoML-GA) coupled with computational fluid dynamics simulations for rapid engine design optimization—0
Active learning for object detection in high-resolution satellite images—0
Diminishing Uncertainty within the Training Pool: Active Learning for Medical Image Segmentation—0
Stochastic Optimization for Vaccine and Testing Kit Allocation for the COVID-19 Pandemic—0
Semi-Supervised Active Learning for Semi-Supervised Models: Exploit Adversarial Examples With Graph-Based Virtual Labels—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Learning to Make Decisions via Submodular Regularization—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
On the Geometry of Deep Bayesian Active Learning—0
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation—0
Active Learning for Lane Detection: A Knowledge Distillation Approach—0
Efficiently labelling sequences using semi-supervised active learning—0
Learning Rare Category Classifiers on a Tight Labeling Budget—0
Least Probable Disagreement Region for Active Learning—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Active Universal Domain Adaptation—0
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Crowd Counting With Partial Annotations in an ImageCode0
Contrastive Coding for Active Learning Under Class Distribution Mismatch—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Whom to Test? Active Sampling Strategies for Managing COVID-19—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Learning Halfspaces With Membership Queries—0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise—0
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications—0
Minimax Active Learning—0
Rebuilding Trust in Active Learning with Actionable Metrics—0
Embodied Visual Active Learning for Semantic Segmentation—0
Learning active learning at the crossroads? evaluation and discussion—0
Active Learning for Deep Gaussian Process SurrogatesCode0
Chernoff Sampling for Active Testing and Extension to Active Regression—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
Active Hierarchical Imitation and Reinforcement Learning—0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—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