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 551–600 of 3073 papers

TitleStatusHype
ActiveAnno3D -- An Active Learning Framework for Multi-Modal 3D Object DetectionCode4
Active Learning for Graphs with Noisy Structures—0
Foundation Model Makes Clustering A Better Initialization For Cold-Start Active LearningCode0
Composite Active Learning: Towards Multi-Domain Active Learning with Theoretical GuaranteesCode0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Conditional Normalizing Flows for Active Learning of Coarse-Grained Molecular RepresentationsCode0
Automatic Segmentation of the Spinal Cord Nerve RootletsCode0
ActDroid: An active learning framework for Android malware detection—0
The Why, When, and How to Use Active Learning in Large-Data-Driven 3D Object Detection for Safe Autonomous Driving: An Empirical Exploration—0
Active learning of Boltzmann samplers and potential energies with quantum mechanical accuracy—0
SelectLLM: Can LLMs Select Important Instructions to Annotate?Code1
Breaking the Barrier: Selective Uncertainty-based Active Learning for Medical Image SegmentationCode0
A Study of Acquisition Functions for Medical Imaging Deep Active LearningCode0
Graph-based Active Learning for Entity Cluster Repair—0
Revisiting Active Learning in the Era of Vision Foundation ModelsCode1
Multitask Active Learning for Graph Anomaly DetectionCode0
Learning from the Best: Active Learning for Wireless Communications—0
MORPH: Towards Automated Concept Drift Adaptation for Malware Detection—0
Falcon: Fair Active Learning using Multi-armed BanditsCode0
Navigating the Maize: Cyclic and conditional computational graphs for molecular simulation—0
Querying Easily Flip-flopped Samples for Deep Active LearningCode1
Improving Classification Performance With Human Feedback: Label a few, we label the rest—0
Ship Detection in SAR Images with Human-in-the-Loop—0
ValUES: A Framework for Systematic Validation of Uncertainty Estimation in Semantic SegmentationCode1
A Reproducibility Study of Goldilocks: Just-Right Tuning of BERT for TARCode0
Calpric: Inclusive and Fine-grain Labeling of Privacy Policies with Crowdsourcing and Active LearningCode0
Compute-Efficient Active LearningCode0
Harnessing the Power of Beta Scoring in Deep Active Learning for Multi-Label Text Classification—0
Active Learning for NLP with Large Language Models—0
An Experimental Design Framework for Label-Efficient Supervised Finetuning of Large Language Models—0
Phase discovery with active learning: Application to structural phase transitions in equiatomic NiTi—0
Inconsistency-Based Data-Centric Active Open-Set AnnotationCode1
Advancing Deep Active Learning & Data Subset Selection: Unifying Principles with Information-Theory Intuitions—0
The Role of Higher-Order Cognitive Models in Active Learning—0
TeLeS: Temporal Lexeme Similarity Score to Estimate Confidence in End-to-End ASRCode0
Zero-shot Active Learning Using Self Supervised Learning—0
Epistemic Uncertainty Quantification For Pre-Trained Neural Networks—0
Weakly Supervised Point Cloud Semantic Segmentation via Artificial OracleCode1
Active Domain Adaptation with False Negative Prediction for Object Detection—0
ANALYTiC: Understanding Decision Boundaries and Dimensionality Reduction in Machine Learning—0
Quantifying Policy Administration Cost in an Active Learning Framework—0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
DeLR: Active Learning for Detection with Decoupled Localization and Recognition Query—0
Active Third-Person Imitation Learning—0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Optimal Decision Tree and Adaptive Submodular Ranking with Noisy Outcomes—0
MEAOD: Model Extraction Attack against Object Detectors—0
Entropic Open-set Active LearningCode1
On the Convergence of Loss and Uncertainty-based Active Learning AlgorithmsCode0
Generalized Category Discovery with Large Language Models in the LoopCode1
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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