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 1501–1550 of 3073 papers

TitleStatusHype
Physics-enhanced deep surrogates for partial differential equations—0
An Interactive Visualization Tool for Understanding Active LearningCode0
Deep Unsupervised Active Learning on Learnable Graphs—0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Focusing on Potential Named Entities During Active Label AcquisitionCode1
Contextual Bayesian optimization with binary outputs—0
Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational DataCode1
Active Learning for Rumor Identification on Social Media—0
Partial-Adaptive Submodular Maximization—0
Convergence of Uncertainty Sampling for Active Learning—0
On the use of uncertainty in classifying Aedes Albopictus mosquitoes—0
RIM: Reliable Influence-based Active Learning on GraphsCode0
Teaching an Active Learner with Contrastive Examples—0
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Failure-averse Active Learning for Physics-constrained Systems—0
Active-LATHE: An Active Learning Algorithm for Boosting the Error Exponent for Learning Homogeneous Ising TreesCode0
Diversity Enhanced Active Learning with Strictly Proper Scoring RulesCode1
Confidence-Aware Active Feedback for Interactive Instance SearchCode0
A Simple Baseline for Low-Budget Active LearningCode1
GeneDisco: A Benchmark for Experimental Design in Drug DiscoveryCode1
Domain Adaptation and Active Learning for Fine-Grained Recognition in the Field of Biodiversity—0
Single-Modal Entropy based Active Learning for Visual Question Answering—0
Does Data Repair Lead to Fair Models? Curating Contextually Fair Data To Reduce Model BiasCode0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
A-Optimal Active LearningCode0
Active Learning for Deep Visual Tracking—0
On the Importance of Effectively Adapting Pretrained Language Models for Active Learning—0
Should We Trust This Summary? Bayesian Abstractive Summarization to The Rescue—0
Deep Active Learning by Leveraging Training Dynamics—0
Nuances in Margin Conditions Determine Gains in Active Learning—0
Knowledge-driven Active LearningCode0
Streaming Machine Learning and Online Active Learning for Automated Visual Inspection—0
An active learning approach for improving the performance of equilibrium based chemical simulations—0
Active Learning for Improved Semi-Supervised Semantic Segmentation in Satellite ImagesCode1
Model-Change Active Learning in Graph-Based Semi-Supervised LearningCode1
ActiveEA: Active Learning for Neural Entity AlignmentCode0
Fast Posterior Estimation of Cardiac Electrophysiological Model Parameters via Bayesian Active Learning—0
Real-Time Learning from An Expert in Deep Recommendation Systems with Marginal Distance Probability Distribution—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Bayesian Active Summarization—0
Class-Balanced Active Learning for Image ClassificationCode1
Synthesizing Video Trajectory Queries—0
Active Learning of Neural Collision Handler for Complex 3D Mesh Deformations—0
Opportunities for Machine Learning to Accelerate Halide Perovskite Commercialization and Scale-Up—0
Active learning for interactive satellite image change detection—0
Spectroscopy Approaches for Food Safety Applications: Improving Data Efficiency Using Active Learning and Semi-Supervised Learning—0
Addressing practical challenges in Active Learning via a hybrid query strategy—0
Hitting the Target: Stopping Active Learning at the Cost-Based OptimumCode1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
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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