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 2001–2025 of 3073 papers

TitleStatusHype
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
PAL : Pretext-based Active Learning—0
Learning to Actively Learn: A Robust Approach—0
Active Learning for Noisy Data Streams Using Weak and Strong Labelers—0
Graph-based Reinforcement Learning for Active Learning in Real Time: An Application in Modeling River Networks—0
What can be learned from satisfaction assessments?—0
A Survey on Curriculum Learning—0
Improving Classification through Weak Supervision in Context-specific Conversational Agent Development for Teacher Education—0
Pool-based sequential active learning with multi kernels—0
Robot Design With Neural Networks, MILP Solvers and Active Learning—0
Exploiting Context for Robustness to Label Noise in Active Learning—0
ReGAL: Rule-Generative Active Learning for Model-in-the-Loop Weak Supervision—0
On the Utility of Active Instance Selection for Few-Shot Learning—0
ALdataset: a benchmark for pool-based active learning—0
Uncertainty Based Active Learning Strategy for Interactive Weakly Supervised Learning through Data Programming—0
Robust Active Learning Strategies for Model Variability—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise Tasks—0
Identifying Wrongly Predicted Samples: A Method for Active Learning—0
Meta-Active Learning for Node Response Prediction in Graphs—0
Active learning with RESSPECT: Resource allocation for extragalactic astronomical transientsCode0
Pre-trained Language Model Based Active Learning for Sentence Matching—0
On the Importance of Adaptive Data Collection for Extremely Imbalanced Pairwise TasksCode0
Zero-shot Active Learning with Topological Clustering for Multiclass Classification—0
Model Exploration with Cost-Aware Learning—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—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