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 2701–2750 of 3073 papers

TitleStatusHype
A Survey on Multi-Task LearningCode0
Deep Active Learning for Named Entity RecognitionCode1
On Measuring and Quantifying Performance: Error Rates, Surrogate Loss, and an Example in SSL—0
Predicting the Quality of Short Narratives from Social Media—0
A Tutorial on Thompson SamplingCode1
The Impact of Typicality for Informative Representative Selection—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks—0
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback—0
Active Sentiment Domain Adaptation—0
Detecting annotation noise in automatically labelled data—0
Probabilistic Active Learning of Functions in Structural Causal Models—0
Bayesian Semisupervised Learning with Deep Generative Models—0
A Meta-Learning Approach to One-Step Active Learning—0
Uncertainty Decomposition in Bayesian Neural Networks with Latent Variables—0
A Variance Maximization Criterion for Active LearningCode0
Active learning in annotating micro-blogs dealing with e-reputationCode0
Suggestive Annotation: A Deep Active Learning Framework for Biomedical Image Segmentation—0
A strong converse bound for multiple hypothesis testing, with applications to high-dimensional estimation—0
Nuclear Discrepancy for Active Learning—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Active learning machine learns to create new quantum experiments—0
Non-Uniform Subset Selection for Active Learning in Structured DataCode0
Bayesian Pool-based Active Learning With Abstention Feedbacks—0
Unfolding Hidden Barriers by Active Enhanced Sampling—0
Active Learning for Graph EmbeddingCode0
Comments on the proof of adaptive submodular function minimization—0
PANFIS++: A Generalized Approach to Evolving Learning—0
Model Transfer for Tagging Low-resource Languages using a Bilingual DictionaryCode0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow—0
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
Mining Object Parts from CNNs via Active Question-Answering—0
Active classification with comparison queries—0
Parsimonious Random Vector Functional Link Network for Data Streams—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—0
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models—0
Adaptive Gaussian process approximation for Bayesian inference with expensive likelihood functionsCode0
Goal-Driven Dynamics Learning via Bayesian Optimization—0
Sample and Computationally Efficient Learning Algorithms under S-Concave Distributions—0
Episode-Based Active Learning with Bayesian Neural Networks—0
Active Decision Boundary Annotation with Deep Generative ModelsCode0
The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient—0
Adaptivity to Noise Parameters in Nonparametric Active Learning—0
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis—0
Learning Active Learning from DataCode0
Deep Bayesian Active Learning with Image DataCode0
Active Learning for Cost-Sensitive Classification—0
Active Learning for Accurate Estimation of Linear Models—0
Active Learning Using Uncertainty InformationCode0
Diameter-Based Active Learning—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