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 2851–2900 of 3073 papers

TitleStatusHype
Efficient Classifier Training to Minimize False Merges in Electron Microscopy Segmentation—0
Multi-Class Multi-Annotator Active Learning With Robust Gaussian Process for Visual Recognition—0
NEXT: A System for Real-World Development, Evaluation, and Application of Active Learning—0
An Active Learning Framework using Sparse-Graph Codes for Sparse Polynomials and Graph Sketching—0
Convergence rates of sub-sampled Newton methods—0
Gaussian Process Planning with Lipschitz Continuous Reward Functions: Towards Unifying Bayesian Optimization, Active Learning, and Beyond—0
Near-Optimal Active Learning of Multi-Output Gaussian ProcessesCode0
The Unreasonable Effectiveness of Noisy Data for Fine-Grained RecognitionCode0
QBDC: Query by dropout committee for training deep supervised architecture—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Fine-Grained Product Class Recognition for Assisted Shopping—0
Active Learning from Weak and Strong Labelers—0
Active Transfer Learning with Zero-Shot Priors: Reusing Past Datasets for Future Tasks—0
Learning in Unlabeled Networks - An Active Learning and Inference Approach—0
Distance-Penalized Active Learning Using Quantile Search—0
Incremental Active Opinion Learning Over a Stream of Opinionated Documents—0
Improving Event Detection with Active Learning—0
Efficient Named Entity Annotation through Pre-empting—0
Experiments on Active Learning for Croatian Word Sense Disambiguation—0
Fast and easy language understanding for dialog systems with Microsoft Language Understanding Intelligent Service (LUIS)—0
Multi-armed Bandit Problem with Known Trend—0
Introducing Geometry in Active Learning for Image Segmentation—0
Recognizing Extended Spatiotemporal Expressions by Actively Trained Average Perceptron Ensembles—0
From Cutting Planes Algorithms to Compression Schemes and Active Learning—0
Multi-Label Active Learning from Crowds—0
Active Learning for Entity Filtering in Microblog StreamsCode0
Task Selection for Bandit-Based Task Assignment in Heterogeneous Crowdsourcing—0
Upper-Confidence-Bound Algorithms for Active Learning in Multi-Armed Bandits—0
ALEVS: Active Learning by Statistical Leverage Sampling—0
A System for Generating Multiple Choice Questions: With a Novel Approach for Sentence Selection—0
Learning Salient Samples and Distributed Representations for Topic-Based Chinese Message Polarity Classification—0
Combining Active Learning and Partial Annotation for Domain Adaptation of a Japanese Dependency Parser—0
Can Natural Language Processing Become Natural Language Coaching?—0
S2: An Efficient Graph Based Active Learning Algorithm with Application to Nonparametric Classification—0
Efficient and Parsimonious Agnostic Active Learning—0
Bayesian Dark KnowledgeCode0
Convergence Rates of Active Learning for Maximum Likelihood Estimation—0
Strat\'egies de s\'election des exemples pour l'apprentissage actif avec des champs al\'eatoires conditionnels—0
An Analytic and Empirical Evaluation of Return-on-Investment-Based Active Learning—0
Judging the Quality of Automatically Generated Gap-fill Question using Active Learning—0
What I've learned about annotating informal text (and why you shouldn't take my word for it)—0
ICE: Rapid Information Extraction Customization for NLP Novices—0
Narrowing the Loop: Integration of Resources and Linguistic Dataset Development with Interactive Machine Learning—0
Efficient Label Collection for Unlabeled Image Datasets—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Active Learning and Discovery of Object Categories in the Presence of Unnameable Instances—0
Learning with a Drifting Target Concept—0
Algorithmic Connections Between Active Learning and Stochastic Convex Optimization—0
An Analysis of Active Learning With Uniform Feature Noise—0
Active learning for sense annotation—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