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 451–475 of 3073 papers

TitleStatusHype
Interactive Ontology Matching with Cost-Efficient Learning—0
AI-Guided Feature Segmentation Techniques to Model Features from Single Crystal Diamond Growth—0
AI-Guided Defect Detection Techniques to Model Single Crystal Diamond Growth—0
AnchorAL: Computationally Efficient Active Learning for Large and Imbalanced DatasetsCode1
Active Test-Time Adaptation: Theoretical Analyses and An AlgorithmCode1
Focused Active Learning for Histopathological Image Classification—0
ProtoAL: Interpretable Deep Active Learning with prototypes for medical imagingCode0
Active Causal Learning for Decoding Chemical Complexities with Targeted Interventions—0
Conversational Disease Diagnosis via External Planner-Controlled Large Language ModelsCode0
Active learning for efficient annotation in precision agriculture: a use-case on crop-weed semantic segmentation—0
LLMs in the Loop: Leveraging Large Language Model Annotations for Active Learning in Low-Resource LanguagesCode0
Active Exploration in Bayesian Model-based Reinforcement Learning for Robot Manipulation—0
Hallucination Diversity-Aware Active Learning for Text Summarization—0
Uncertainty-aware Active Learning of NeRF-based Object Models for Robot Manipulators using Visual and Re-orientation Actions—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
Using Chao's Estimator as a Stopping Criterion for Technology-Assisted Review—0
Collaborative Active Learning in Conditional Trust Environment—0
Active Learning of Dynamics Using Prior Domain Knowledge in the Sampling Process—0
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
Generative Active Learning for Image Synthesis PersonalizationCode0
Modular Deep Active Learning Framework for Image Annotation: A Technical Report for the Ophthalmo-AI Project—0
CODA: A COst-efficient Test-time Domain Adaptation Mechanism for HAR—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