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 851–900 of 3073 papers

TitleStatusHype
A Unified Approach Towards Active Learning and Out-of-Distribution Detection—0
ALARM: Active LeArning of Rowhammer Mitigations—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
Active Learning for Video Classification with Frame Level Queries—0
ALANNO: An Active Learning Annotation System for Mortals—0
Active Learning with Constrained Topic Model—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
A Contextual Bandit Approach for Stream-Based Active Learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
A Lagrangian Duality Approach to Active Learning—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Comprehensive Evaluation of OCT-based Automated Segmentation of Retinal Layer, Fluid and Hyper-Reflective Foci: Impact on Diabetic Retinopathy Severity Assessment—0
Active Learning for Undirected Graphical Model Selection—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
AKF-SR: Adaptive Kalman Filtering-based Successor Representation—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
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Learning for Saddle Point Calculation—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
Autonomous Emergency Braking With Driver-In-The-Loop: Torque Vectoring for Active Learning—0
Autonomous synthesis of metastable materials—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
AutoSciLab: A Self-Driving Laboratory For Interpretable Scientific Discovery—0
AutoTandemML: Active Learning Enhanced Tandem Neural Networks for Inverse Design Problems—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Bucketized Active Sampling for Learning ACOPF—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
Active Learning with Neural Networks: Insights from Nonparametric Statistics—0
A Wholistic View of Continual Learning with Deep Neural Networks: Forgotten Lessons and the Bridge to Active and Open World Learning—0
Accelerating the Training and Improving the Reliability of Machine-Learned Interatomic Potentials for Strongly Anharmonic Materials through Active Learning—0
Active Learning with Oracle Epiphany—0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
AI For Fraud Awareness—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
BAOD: Budget-Aware Object Detection—0
Active Learning for Structured Prediction from Partially Labeled Data—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