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 1226–1250 of 3073 papers

TitleStatusHype
Active Learning for Abstractive Text SummarizationCode0
A domain-decomposed VAE method for Bayesian inverse problems—0
Active Learning Guided by Efficient Surrogate Learners—0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Hybrid Active Learning via Deep Clustering for Video Action Detection—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Deep Active Learning Using Barlow Twins—0
Active Learning for Neural Machine TranslationCode0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Gaussian Process Classification Bandits—0
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders—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
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender SystemsCode0
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis—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