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 1801–1850 of 3073 papers

TitleStatusHype
Deep Active Learning for Multi-Label Classification of Remote Sensing Images—0
Deep active learning for nonlinear system identification—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
Deep Active Learning in the Open World—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Deep Active Learning over the Long Tail—0
Deep Active Learning Using Barlow Twins—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
Deep Active Learning with Noise Stability—0
Deep Active Learning with Noisy Oracle in Object Detection—0
Deep Active Learning with Structured Neural Depth Search—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
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
Deep Deterministic Uncertainty: A New Simple Baseline—0
Deep Ensemble Bayesian Active Learning : Adressing the Mode Collapse issue in Monte Carlo dropout via Ensembles—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
Deep Kernel Methods Learn Better: From Cards to Process Optimization—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—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
Defect Classification in Additive Manufacturing Using CNN-Based Vision Processing—0
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query—0
DEMAU: Decompose, Explore, Model and Analyse Uncertainties—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
Designing and Contextualising Probes for African Languages—0
Design of an Active Learning System with Human Correction for Content Analysis—0
Detecting annotation noise in automatically labelled data—0
Detecting Missing Annotation Disagreement using Eye Gaze Information—0
Detecting Mitoses with a Convolutional Neural Network for MIDOG 2022 Challenge—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