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 1101–1150 of 3073 papers

TitleStatusHype
Active learning for deep semantic parsing—0
ActiveDP: Bridging Active Learning and Data Programming—0
AutoNLU: Detecting, root-causing, and fixing NLU model errors—0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
Automatic quantification of breast cancer biomarkers from multiple 18F-FDG PET image segmentation—0
Automatic Playtesting for Game Parameter Tuning via Active Learning—0
Active Learning with Importance Sampling—0
Active Learning for Deep Object Detection—0
Automatic Learning to Detect Concept Drift—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Active Learning for Deep Neural Networks on Edge Devices—0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
Automated Testing of Spatially-Dependent Environmental Hypotheses through Active Transfer Learning—0
Active Learning with Expert Advice—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 with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Automated Image Analysis Framework for the High-Throughput Determination of Grapevine Berry Sizes Using Conditional Random Fields—0
Automated Gain Control Through Deep Reinforcement Learning for Downstream Radar Object Detection—0
Active Learning with Effective Scoring Functions for Semi-Supervised Temporal Action Localization—0
Active Learning for Deep Learning-Based Hemodynamic Parameter Estimation—0
Active Domain Adaptation with False Negative Prediction for Object Detection—0
A critical look at the current train/test split in machine learning—0
Automated Discovery of Pairwise Interactions from Unstructured Data—0
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