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 1201–1250 of 3073 papers

TitleStatusHype
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Active Learning For Repairable Hardware Systems With Partial Coverage—0
DADO -- Low-Cost Query Strategies for Deep Active Design Optimization—0
DART: A Lightweight Quality-Suggestive Data-to-Text Annotation Tool—0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Constrained Bayesian Active Learning of Interference Channels in Cognitive Radio Networks—0
Data Distillation for Neural Network Potentials toward Foundational Dataset—0
Data driven semi-supervised learning—0
Importance sampling based active learning for parametric seismic fragility curve estimation—0
Data-driven discovery of free-form governing differential equations—0
Data-driven surrogate modelling and benchmarking for process equipment—0
Consistent Training via Energy-Based GFlowNets for Modeling Discrete Joint Distributions—0
Data-efficient Active Learning for Structured Prediction with Partial Annotation and Self-Training—0
Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification—0
Active Learning for Non-Parametric Choice Models—0
Data Efficient Lithography Modeling with Transfer Learning and Active Data Selection—0
A Finite-Horizon Approach to Active Level Set Estimation—0
Data-efficient Online Classification with Siamese Networks and Active Learning—0
Consistency-based Semi-supervised Active Learning: Towards Minimizing Labeling Cost—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Adaptive Defective Area Identification in Material Surface Using Active Transfer Learning-based Level Set Estimation—0
Data Shapley Valuation for Efficient Batch Active Learning—0
Data Summarization via Bilevel Optimization—0
Data Uncertainty without Prediction Models—0
Accelerating engineering design by automatic selection of simulation cases through Pool-Based Active Learning—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
Deep Bayesian Active-Learning-to-Rank for Endoscopic Image Data—0
Congruence-based Learning of Probabilistic Deterministic Finite Automata—0
Adaptive Combinatorial Maximization: Beyond Approximate Greedy Policies—0
DECAL: DEployable Clinical Active Learning—0
Deciding when to stop: Efficient stopping of active learning guided drug-target prediction—0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost—0
ConfusionFlow: A model-agnostic visualization for temporal analysis of classifier confusion—0
Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning—0
Deep Active Learning for Anomaly Detection—0
Active Learning for Single Neuron Models with Lipschitz Non-Linearities—0
Deep Active Ensemble Sampling For Image Classification—0
When Contrastive Learning Meets Active Learning: A Novel Graph Active Learning Paradigm with Self-Supervision—0
Deep Active Learning: A Reality Check—0
Deep Active Learning based Experimental Design to Uncover Synergistic Genetic Interactions for Host Targeted Therapeutics—0
Deep Active Learning by Leveraging Training Dynamics—0
Deep Active Learning by Model Interpretability—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Adaptive Active Learning for Image Classification—0
Active Learning for Nonlinear System Identification with Guarantees—0
Deep Active Learning for Computer Vision: Past and Future—0
Deep Active Learning for Data Mining from Conflict Text Corpora—0
Confident Coreset for Active Learning in Medical Image Analysis—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