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 301–350 of 3073 papers

TitleStatusHype
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
Confidence-Aware Learning for Deep Neural NetworksCode1
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
Counting People by Estimating People FlowsCode1
cRedAnno+: Annotation Exploitation in Self-Explanatory Lung Nodule DiagnosisCode1
CriticLean: Critic-Guided Reinforcement Learning for Mathematical FormalizationCode1
Active Bayesian Causal InferenceCode1
Data-Driven Autoencoder Numerical Solver with Uncertainty Quantification for Fast Physical SimulationsCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
Bayesian Model-Agnostic Meta-LearningCode1
Deep Active Learning for Axon-Myelin Segmentation on Histology DataCode1
Deep Active Learning for Biased Datasets via Fisher Kernel Self-SupervisionCode1
Deep Active Learning for Named Entity RecognitionCode1
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
Multiple instance active learning for object detectionCode1
Active Learning for Imbalanced Civil Infrastructure Data—0
Active Learning for Identifying Disaster-Related Tweets: A Comparison with Keyword Filtering and Generic Fine-Tuning—0
Active Generative Adversarial Network for Image Classification—0
Active Learning for Identification of Linear Dynamical Systems—0
Active Learning for Human Pose Estimation—0
Active Altruism Learning and Information Sufficiency for Autonomous Driving—0
Active Learning Principles for In-Context Learning with Large Language Models—0
Active Learning for High-Dimensional Binary Features—0
Active Learning for Graphs with Noisy Structures—0
Active Learning for Graph Neural Networks via Node Feature Propagation—0
Active Algorithms For Preference Learning Problems with Multiple Populations—0
LLMs as Probabilistic Minimally Adequate Teachers for DFA Learning—0
Active Learning Polynomial Threshold Functions—0
Active Learning: Problem Settings and Recent Developments—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active Learning for Finely-Categorized Image-Text Retrieval by Selecting Hard Negative Unpaired Samples—0
Active Foundational Models for Fault Diagnosis of Electrical Motors—0
Active Learning for Fine-Grained Sketch-Based Image Retrieval—0
Active Learning for Financial Investment Reports—0
Active Finite Reward Automaton Inference and Reinforcement Learning Using Queries and Counterexamples—0
Active Adversarial Domain Adaptation—0
Active learning for fast and slow modeling attacks on Arbiter PUFs—0
Active Learning for Fair and Stable Online Allocations—0
Active Fine-Tuning from gMAD Examples Improves Blind Image Quality Assessment—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
ActiveAD: Planning-Oriented Active Learning for End-to-End Autonomous Driving—0
Active Learning for Binary Classification with Abstention—0
Active Learning Ranking from Pairwise Preferences with Almost Optimal Query Complexity—0
Active learning for enumerating local minima based on Gaussian process derivatives—0
Active Few-Shot Fine-Tuning—0
Active Learning On Weighted Graphs Using Adaptive And Non-adaptive Approaches—0
Active Few-Shot Classification: a New Paradigm for Data-Scarce Learning Settings—0
Active learning for energy-based antibody optimization and enhanced screening—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