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 1451–1475 of 3073 papers

TitleStatusHype
SALAD: Source-free Active Label-Agnostic Domain Adaptation for Classification, Segmentation and DetectionCode0
Active Domain Adaptation with Multi-level Contrastive Units for Semantic Segmentation—0
Tyger: Task-Type-Generic Active Learning for Molecular Property Prediction—0
CELEST: Federated Learning for Globally Coordinated Threat Detection—0
Tackling Provably Hard Representative Selection via Graph Neural Networks—0
A Simple yet Effective Framework for Active Learning to Rank—0
Mask-guided Vision Transformer (MG-ViT) for Few-Shot Learning—0
Probability trees and the value of a single intervention—0
Marginal and Joint Cross-Entropies & Predictives for Online Bayesian Inference, Active Learning, and Active Sampling—0
Active learning of causal probability trees—0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
SCAF: Skip-Connections in Auto-encoder for Face alignment with few annotated data—0
ALLSH: Active Learning Guided by Local Sensitivity and Hardness—0
The Right Tool for the Job: Matching Active Learning Techniques to Learning ObjectivesCode0
One Size Does Not Fit All: The Case for Personalised Word Complexity Models—0
Polynomial-Time Algorithms for Counting and Sampling Markov Equivalent DAGs with ApplicationsCode0
FINETUNA: Fine-tuning Accelerated Molecular Simulations—0
Uncertainty Estimation of Transformer Predictions for Misclassification DetectionCode0
A Word-and-Paradigm Workflow for Fieldwork Annotation—0
LeaningTower@LT-EDI-ACL2022: When Hope and Hate Collide—0
A Comparison of Strategies for Source-Free Domain AdaptationCode0
Predicting Difficulty and Discrimination of Natural Language Questions—0
From Limited Annotated Raw Material Data to Quality Production Data: A Case Study in the Milk Industry (Technical Report)—0
Data Uncertainty without Prediction Models—0
Label a Herd in Minutes: Individual Holstein-Friesian Cattle IdentificationCode0
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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