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 601–650 of 3073 papers

TitleStatusHype
MOBIUS: Towards the Next Generation of Query-Ad Matching in Baidu's Sponsored Search—0
An incremental preference elicitation-based approach to learning potentially non-monotonic preferences in multi-criteria sorting—0
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait SketchingCode0
Adaptive Open-Set Active Learning with Distance-Based Out-of-Distribution Detection for Robust Task-Oriented Dialog SystemCode0
Facilitating AI and System Operator Synergy: Active Learning-Enhanced Digital Twin Architecture for Day-Ahead Load Forecasting—0
Targeting the partition function of chemically disordered materials with a generative approach based on inverse variational autoencoders—0
From catch-up to frontier: The utility model as a learning device to escape the middle-income trap—0
Understanding Uncertainty-based Active Learning Under Model Mismatch—0
ESA: Annotation-Efficient Active Learning for Semantic SegmentationCode0
Semi-Supervised Variational Adversarial Active Learning via Learning to Rank and Agreement-Based Pseudo Labeling—0
Human-In-The-Loop Machine Learning for Safe and Ethical Autonomous Vehicles: Principles, Challenges, and Opportunities—0
Active learning for efficient data selection in radio-signal based positioning via deep learning—0
Active Learning of Molecular Data for Task-Specific ObjectivesCode0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active learning of digenic functions with boolean matrix logic programming—0
Gravix: Active Learning for Gravitational Waves Classification Algorithms—0
Unlocking the Power of LLM Uncertainty for Active In-Context Example Selection—0
Robust Offline Active Learning on GraphsCode0
Robust Active Learning (RoAL): Countering Dynamic Adversaries in Active Learning with Elastic Weight Consolidation—0
Reciprocal Learning—0
Hyperbolic Learning with Multimodal Large Language Models—0
Avoid Wasted Annotation Costs in Open-set Active Learning with Pre-trained Vision-Language Model—0
SegXAL: Explainable Active Learning for Semantic Segmentation in Driving Scene Scenarios—0
Leveraging Variation Theory in Counterfactual Data Augmentation for Optimized Active Learning—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
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