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 1126–1150 of 3073 papers

TitleStatusHype
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Auto-Differentiating Linear Algebra—0
Active Learning with Context Sampling and One-vs-Rest Entropy for Semantic Segmentation—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
A User Study of Perceived Carbon Footprint—0
Active Learning with Constrained Topic Model—0
Active Learning with Combinatorial Coverage—0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Active learning with biased non-response to label requests—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
Active Learning with a Drifting Distribution—0
Active Learning for Crowd-Sourced Databases—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Active Learning via Regression Beyond Realizability—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness—0
Active Learning for Cost-Sensitive Classification—0
Active Discriminative Text Representation Learning—0
A Contextual Bandit Approach for Stream-Based Active Learning—0
Auditing and Robustifying COVID-19 Misinformation Datasets via Anticontent Sampling—0
Auditing: Active Learning with Outcome-Dependent Query Costs—0
Audio-Enhanced Vision-Language Modeling with Latent Space Broadening for High Quality Data Expansion—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