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 2951–3000 of 3073 papers

TitleStatusHype
Parallel FDA5 for Fast Deployment of Accurate Statistical Machine Translation Systems—0
Improving Classification-Based Natural Language Understanding with Non-Expert Annotation—0
Optimizing Features in Active Machine Learning for Complex Qualitative Content Analysis—0
Semantics for Large-Scale Multimedia: New Challenges for NLP—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Active Learning with Efficient Feature Weighting Methods for Improving Data Quality and Classification Accuracy—0
Difficult Cases: From Data to Learning, and Back—0
Automatic Annotation Suggestions and Custom Annotation Layers in WebAnno—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Incremental Activity Modeling and Recognition in Streaming Videos—0
Active Semi-Supervised Learning Using Sampling Theory for Graph SignalsCode0
Focusing Annotation for Semantic Role Labeling—0
A Quality-based Active Sample Selection Strategy for Statistical Machine Translation—0
Language Resource Addition: Dictionary or Corpus?—0
Active Learning for Undirected Graphical Model Selection—0
A Compression Technique for Analyzing Disagreement-Based Active Learning—0
Confidence-based Active Learning Methods for Machine Translation—0
Domain Adaptation with Active Learning for Coreference Resolution—0
Active Learning for Post-Editing Based Incrementally Retrained MT—0
Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction—0
Near Optimal Bayesian Active Learning for Decision Making—0
Selective Sampling with Drift—0
Human Activity Recognition using Smartphone—0
Toward Supervised Anomaly Detection—0
An Active Learning Approach for Jointly Estimating Worker Performance and Annotation Reliability with Crowdsourced Data—0
Segmentation for Efficient Supervised Language Annotation with an Explicit Cost-Utility Tradeoff—0
Active Discovery of Network Roles for Predicting the Classes of Network Nodes—0
Active Player Modelling—0
Latent Structured Active Learning—0
Statistical Active Learning Algorithms—0
Active Learning for Probabilistic Hypotheses Using the Maximum Gibbs Error Criterion—0
Buy-in-Bulk Active Learning—0
Σ-Optimality for Active Learning on Gaussian Random Fields—0
Recommending with an Agenda: Active Learning of Private Attributes using Matrix Factorization—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
Active Learning for Dependency Parsing by A Committee of Parsers—0
Para-active learning—0
Active Learning of Linear Embeddings for Gaussian Processes—0
Advances in Hyperspectral Image Classification: Earth monitoring with statistical learning methods—0
An Efficient Active Learning Framework for New Relation Types—0
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Reserved Self-training: A Semi-supervised Sentiment Classification Method for Chinese Microblogs—0
Detecting Missing Annotation Disagreement using Eye Gaze Information—0
Active Learning with Expert Advice—0
Building Bridges: Viewing Active Learning from the Multi-Armed Bandit Lens—0
Sequential Design for Optimal Stopping Problems—0
Using memristor crossbar structure to implement a novel adaptive real time fuzzy modeling algorithm—0
Decision Trees for Function Evaluation - Simultaneous Optimization of Worst and Expected Cost—0
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Active Learning for Phenotyping Tasks—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