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 1026–1050 of 3073 papers

TitleStatusHype
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Hybrid Active Learning via Deep Clustering for Video Action Detection—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Deep Active Learning Using Barlow Twins—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Active Learning for Neural Machine TranslationCode0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders—0
Gaussian Process Classification Bandits—0
An active learning method for solving competitive multi-agent decision-making and control problemsCode0
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection—0
Temporal Output Discrepancy for Loss Estimation-based Active Learning—0
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis—0
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender SystemsCode0
POTATO: The Portable Text Annotation ToolCode2
Man-recon: manifold learning for reconstruction with deep autoencoder for smart seismic interpretationCode1
THMA: Tencent HD Map AI System for Creating HD Map Annotations—0
The Infinite Index: Information Retrieval on Generative Text-To-Image Models—0
Pixel is All You Need: Adversarial Trajectory-Ensemble Active Learning for Salient Object Detection—0
ALRt: An Active Learning Framework for Irregularly Sampled Temporal Data—0
An adaptive human-in-the-loop approach to emission detection of Additive Manufacturing processes and active learning with computer vision—0
MAViC: Multimodal Active Learning for Video Captioning—0
Predicting article quality scores with machine learning: The UK Research Excellence Framework—0
Show:102550
← PrevPage 42 of 123Next →

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