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 2651–2675 of 3073 papers

TitleStatusHype
A Pipeline for Post-Crisis Twitter Data Acquisition—0
Localization-Aware Active Learning for Object Detection—0
Active Community Detection with Maximal Expected Model Change—0
Semi-automated Annotation of Signal Events in Clinical EEG Data—0
Few-Shot Learning with Graph Neural NetworksCode0
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification—0
Fast kNN mode seeking clustering applied to active learning—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Noisy Natural Gradient as Variational InferenceCode0
Active Learning from Peers—0
Adaptive Active Hypothesis Testing under Limited Information—0
Active Regression via Linear-Sample Sparsification—0
An Adaptive Strategy for Active Learning with Smooth Decision Boundary—0
Cost-Effective Active Learning for Melanoma SegmentationCode0
Bayesian Active Edge Evaluation on Expensive Graphs—0
Variational Adaptive-Newton Method for Explorative Learning—0
Few-Shot Learning with Graph Neural NetworksCode0
Active Learning for Visual Question Answering: An Empirical StudyCode0
Online Tool Condition Monitoring Based on Parsimonious Ensemble+—0
Deep Active Learning over the Long Tail—0
CADET: Computer Assisted Discovery Extraction and Translation—0
A Novel Ensemble Learning Approach to Unsupervised Record Linkage—0
Dependency Parsing with Partial Annotations: An Empirical Comparison—0
Analyzing Well-Formedness of Syllables in Japanese Sign Language—0
Auto-Differentiating Linear Algebra—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