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 626–650 of 3073 papers

TitleStatusHype
Active Learning for Level Set Estimation Using Randomized Straddle Algorithms—0
Active Learning for WBAN-based Health Monitoring—0
Batch Active Learning in Gaussian Process Regression using Derivatives—0
Bayesian Active Learning for Semantic Segmentation—0
PreMix: Addressing Label Scarcity in Whole Slide Image Classification with Pre-trained Multiple Instance Learning Aggregators—0
A Cross-Domain Benchmark for Active LearningCode0
Learn from the Learnt: Source-Free Active Domain Adaptation via Contrastive Sampling and Visual PersistenceCode0
On the Pros and Cons of Active Learning for Moral Preference Elicitation—0
Amortized Active Learning for Nonparametric Functions—0
A Novel Two-Step Fine-Tuning Pipeline for Cold-Start Active Learning in Text Classification Tasks—0
MILAN: Milli-Annotations for Lidar Semantic Segmentation—0
Exploring and Addressing Reward Confusion in Offline Preference Learning—0
Self-driving lab discovers principles for steering spontaneous emission—0
Enhancing Retinal Disease Classification from OCTA Images via Active Learning TechniquesCode0
Downstream-Pretext Domain Knowledge Traceback for Active Learning—0
Enhancing Graph Neural Networks with Limited Labeled Data by Actively Distilling Knowledge from Large Language Models—0
The Future of Data Science EducationCode0
Generalized Coverage for More Robust Low-Budget Active Learning—0
On Machine Learning Approaches for Protein-Ligand Binding Affinity Prediction—0
Learning Weighted Finite Automata over the Max-Plus Semiring and its Termination—0
Active Learning for Derivative-Based Global Sensitivity Analysis with Gaussian ProcessesCode0
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations—0
Automated Neural Patent Landscaping in the Small Data Regime—0
Pseudo-triplet Guided Few-shot Composed Image Retrieval—0
Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label NoiseCode0
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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