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 751–775 of 3073 papers

TitleStatusHype
Few-shot Named Entity Recognition via Superposition Concept DiscriminationCode0
Enhancing Semi-supervised Domain Adaptation via Effective Target LabelingCode0
On the Fragility of Active Learners for Text ClassificationCode0
An active learning model to classify animal species in Hong Kong—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project—0
DP-Dueling: Learning from Preference Feedback without Compromising User Privacy—0
Active Learning for Regression based on Wasserstein distance and GroupSort Neural Networks—0
Generative Active Learning for Image Synthesis PersonalizationCode0
Annotation-Efficient Polyp Segmentation via Active Learning—0
A Chain-of-Thought Prompting Approach with LLMs for Evaluating Students' Formative Assessment Responses in Science—0
Deep Active Learning: A Reality Check—0
Machine Learning Optimized Approach for Parameter Selection in MESHFREE Simulations—0
Uncertainty Driven Active Learning for Image Segmentation in Underwater Inspection—0
A Comprehensive Review of Latent Space Dynamics Identification Algorithms for Intrusive and Non-Intrusive Reduced-Order-Modeling—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
To Label or Not to Label: Hybrid Active Learning for Neural Machine Translation—0
Deep Submodular Peripteral Networks—0
From Weak to Strong Sound Event Labels using Adaptive Change-Point Detection and Active LearningCode0
Physics-constrained Active Learning for Soil Moisture Estimation and Optimal Sensor Placement—0
Active Generation for Image ClassificationCode0
Evolving Knowledge Distillation with Large Language Models and Active Learning—0
Domain Adversarial Active Learning for Domain Generalization Classification—0
On the Topology Awareness and Generalization Performance of Graph Neural Networks—0
SUPClust: Active Learning at the Boundaries—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