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 1301–1325 of 3073 papers

TitleStatusHype
Deep Kernel Methods Learn Better: From Cards to Process Optimization—0
AL-iGAN: An Active Learning Framework for Tunnel Geological Reconstruction Based on TBM Operational Data—0
Confidence-based Active Learning Methods for Machine Translation—0
Deep Probabilistic Ensembles: Approximate Variational Inference through KL Regularization—0
Deep reinforced active learning for multi-class image classification—0
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Deep Submodular Peripteral Networks—0
Deep Surrogate of Modular Multi Pump using Active Learning—0
Deep Unsupervised Active Learning on Learnable Graphs—0
DeepVisualInsight: Time-Travelling Visualization for Spatio-Temporal Causality of Deep Classification Training—0
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators—0
Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study—0
DEMAU: Decompose, Explore, Model and Analyse Uncertainties—0
Active Learning from Peers—0
Active Learning-based Isolation Forest (ALIF): Enhancing Anomaly Detection in Decision Support Systems—0
Dependency-aware Maximum Likelihood Estimation for Active Learning—0
Dependency Parsing with Partial Annotations: An Empirical Comparison—0
Depression Symptoms Modelling from Social Media Text: A Semi-supervised Learning Approach—0
Depth Uncertainty Networks for Active Learning—0
ALLWAS: Active Learning on Language models in WASserstein space—0
Designing and Contextualising Probes for African Languages—0
Design of an Active Learning System with Human Correction for Content Analysis—0
Cell Library Characterization for Composite Current Source Models Based on Gaussian Process Regression and Active Learning—0
Detecting annotation noise in automatically labelled data—0
Dirichlet-Based Coarse-to-Fine Example Selection For Open-Set Annotation—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