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 1601–1625 of 3073 papers

TitleStatusHype
On Deep Unsupervised Active Learning—0
One-Bit Active Query With Contrastive Pairs—0
One-bit Supervision for Image Classification: Problem, Solution, and Beyond—0
One Class One Click: Quasi Scene-level Weakly Supervised Point Cloud Semantic Segmentation with Active Learning—0
One-Round Active Learning—0
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models—0
One Size Does Not Fit All: The Case for Personalised Word Complexity Models—0
On Label-Efficient Computer Vision: Building Fast and Effective Few-Shot Image Classifiers—0
Online Active Learning for Cost Sensitive Domain Adaptation—0
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders—0
Online Active Learning For Sound Event Detection—0
Disagreement-based Active Learning in Online Settings—0
Online Active Learning of Reject Option Classifiers—0
Online Active Learning with Surrogate Loss Functions—0
Online Active Linear Regression via Thresholding—0
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems—0
Online allocation and homogeneous partitioning for piecewise constant mean-approximation—0
Online Bandit Learning with Offline Preference Data for Improved RLHF—0
Online Body Schema Adaptation through Cost-Sensitive Active Learning—0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision—0
Online Learning of Non-Markovian Reward Models—0
Learning Novel Objects Continually Through Curiosity—0
Online Submodular Set Cover, Ranking, and Repeated Active Learning—0
Online Tool Condition Monitoring Based on Parsimonious Ensemble+—0
On Locality in Graph Learning via Graph Neural Network—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