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 1826–1850 of 3073 papers

TitleStatusHype
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