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 2801–2825 of 3073 papers

TitleStatusHype
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
A Unified Active Learning Framework for Annotating Graph Data with Application to Software Source Code Performance Prediction—0
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
A unified framework for learning with nonlinear model classes from arbitrary linear samples—0
Aurora: Are Android Malware Classifiers Reliable and Stable under Distribution Shift?—0
A User Study of Perceived Carbon Footprint—0
A Utility-Mining-Driven Active Learning Approach for Analyzing Clickstream Sequences—0
Auto-Dialabel: Labeling Dialogue Data with Unsupervised Learning—0
Auto-Differentiating Linear Algebra—0
Automated Detection of GDPR Disclosure Requirements in Privacy Policies using Deep Active Learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—0
Automatic Learning to Detect Concept Drift—0
Automatic Playtesting for Game Parameter Tuning via Active Learning—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Autonomous Assessment of Demonstration Sufficiency via Bayesian Inverse Reinforcement Learning—0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—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