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 376–400 of 3073 papers

TitleStatusHype
Federated Active Learning Framework for Efficient Annotation Strategy in Skin-lesion Classification—0
Understanding active learning of molecular docking and its applicationsCode1
Annotation Cost-Efficient Active Learning for Deep Metric Learning Driven Remote Sensing Image Retrieval—0
Deep Bayesian Active Learning for Preference Modeling in Large Language ModelsCode0
Online Bandit Learning with Offline Preference Data for Improved RLHF—0
Parameter-Efficient Active Learning for Foundational models—0
Self-Training for Sample-Efficient Active Learning for Text Classification with Pre-Trained Language ModelsCode0
Active learning for affinity prediction of antibodies—0
Quantifying Local Model Validity using Active LearningCode0
EFFOcc: A Minimal Baseline for EFficient Fusion-based 3D Occupancy NetworkCode2
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation—0
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and AnnotationCode0
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
Generative Active Learning for Long-tailed Instance SegmentationCode2
Effective Data Selection for Seismic Interpretation through Disagreement—0
Enhancing Generative Molecular Design via Uncertainty-guided Fine-tuning of Variational Autoencoders—0
Exploring Human-in-the-Loop Test-Time Adaptation by Synergizing Active Learning and Model SelectionCode0
Edge-guided and Class-balanced Active Learning for Semantic Segmentation of Aerial Images—0
A Survey of Latent Factor Models in Recommender Systems—0
A Data-Centric Framework for Machine Listening Projects: Addressing Large-Scale Data Acquisition and Labeling through Active Learning—0
Towards Efficient Disaster Response via Cost-effective Unbiased Class Rate Estimation through Neyman Allocation Stratified Sampling Active Learning—0
Salutary Labeling with Zero Human Annotation—0
Entity Alignment with Noisy Annotations from Large Language ModelsCode0
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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