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 676–700 of 3073 papers

TitleStatusHype
Quantifying Local Model Validity using Active LearningCode0
Active learning for affinity prediction of antibodies—0
Greedy SLIM: A SLIM-Based Approach For Preference Elicitation—0
Provably Neural Active Learning Succeeds via Prioritizing Perplexing Samples—0
Simulating, Fast and Slow: Learning Policies for Black-Box Optimization—0
Active ML for 6G: Towards Efficient Data Generation, Acquisition, and AnnotationCode0
"Give Me an Example Like This": Episodic Active Reinforcement Learning from DemonstrationsCode0
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
Entity Alignment with Noisy Annotations from Large Language ModelsCode0
Salutary Labeling with Zero Human Annotation—0
Make Safe Decisions in Power System: Safe Reinforcement Learning Based Pre-decision Making for Voltage Stability Emergency Control—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Lower Bound on the Greedy Approximation Ratio for Adaptive Submodular Cover—0
One-shot Active Learning Based on Lewis Weight Sampling for Multiple Deep Models—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Amortized nonmyopic active search via deep imitation learning—0
Smooth Pseudo-Labeling—0
What Makes Good Few-shot Examples for Vision-Language Models?—0
Enhancing Active Learning for Sentinel 2 Imagery through Contrastive Learning and Uncertainty Estimation—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