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 3051–3073 of 3073 papers

TitleStatusHype
Active Learning from Positive and Unlabeled DataCode0
Active Learning with Gaussian Processes for High Throughput PhenotypingCode0
A Survey of Deep Active LearningCode0
MCDAL: Maximum Classifier Discrepancy for Active LearningCode0
MeaeQ: Mount Model Extraction Attacks with Efficient QueriesCode0
Graph-based Semi-Supervised & Active Learning for Edge FlowsCode0
Graph-boosted Active Learning for Multi-Source Entity ResolutionCode0
Parallel MCMC Without Embarrassing FailuresCode0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
MEAL: Stable and Active Learning for Few-Shot PromptingCode0
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound ImagesCode0
Towards a relation extraction framework for cyber-security conceptsCode0
Greed is Good: Exploration and Exploitation Trade-offs in Bayesian OptimisationCode0
Parameter-Efficient Language Model Tuning with Active Learning in Low-Resource SettingsCode0
Active Learning Framework for Cost-Effective TCR-Epitope Binding Affinity PredictionCode0
Beyond Grids: Multi-objective Bayesian Optimization With Adaptive DiscretizationCode0
TAR on Social Media: A Framework for Online Content ModerationCode0
Task-Aware Active Learning for Endoscopic Image AnalysisCode0
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
Revisiting Sample Size Determination in Natural Language UnderstandingCode0
Active Labeling: Streaming Stochastic GradientsCode0
ABC3: Active Bayesian Causal Inference with Cohn Criteria in Randomized ExperimentsCode0
A framework for fully autonomous design of materials via multiobjective optimization and active learning: challenges and next stepsCode0
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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