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 876–900 of 3073 papers

TitleStatusHype
Real-time Autonomous Control of a Continuous Macroscopic Process as Demonstrated by Plastic Forming—0
Active learning with biased non-response to label requests—0
Fair Active Learning in Low-Data Regimes—0
Advancements in Content-Based Image Retrieval: A Comprehensive Survey of Relevance Feedback Techniques—0
Semi-supervised Active Learning for Video Action DetectionCode0
SE(3)-Invariant Multiparameter Persistent Homology for Chiral-Sensitive Molecular Property Prediction—0
Benchmarking of Query Strategies: Towards Future Deep Active LearningCode0
Bad Students Make Great Teachers: Active Learning Accelerates Large-Scale Visual Understanding—0
A Review of Machine Learning Methods Applied to Video Analysis Systems—0
ODES: Domain Adaptation with Expert Guidance for Online Medical Image Segmentation—0
PALS: Personalized Active Learning for Subjective Tasks in NLPCode0
A Structural-Clustering Based Active Learning for Graph Neural NetworksCode0
Transferable Candidate Proposal with Bounded UncertaintyCode0
Active Learning for Abrupt Shifts Change-point Detection via Derivative-Aware Gaussian Processes—0
Federated Active Learning for Target Domain GeneralisationCode0
Few Clicks Suffice: Active Test-Time Adaptation for Semantic Segmentation—0
ActiveClean: Generating Line-Level Vulnerability Data via Active Learning—0
A Review and A Robust Framework of Data-Efficient 3D Scene Parsing with Traditional/Learned 3D Descriptors—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Towards Comparable Active Learning—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Leveraging deep active learning to identify low-resource mobility functioning information in public clinical notes—0
Comprehensive Benchmarking of Entropy and Margin Based Scoring Metrics for Data Selection—0
The Battleship Approach to the Low Resource Entity Matching ProblemCode0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond—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