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
Active Heteroscedastic Regression—0
Interpretable Active LearningCode0
Learning Algorithms for Active Learning—0
A Survey on Multi-Task LearningCode0
A Nearly Instance Optimal Algorithm for Top-k Ranking under the Multinomial Logit Model—0
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
Joint Optimization of User-desired Content in Multi-document Summaries by Learning from User Feedback—0
Fine-Tuning Convolutional Neural Networks for Biomedical Image Analysis: Actively and Incrementally—0
Active Sentiment Domain Adaptation—0
The Impact of Typicality for Informative Representative Selection—0
Personalizing Gesture Recognition Using Hierarchical Bayesian Neural Networks—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
Unsupervised Clustering and Active Learning of Hyperspectral Images with Nonlinear Diffusion—0
On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow—0
Active classification with comparison queries—0
Mining Object Parts from CNNs via Active Question-Answering—0
Parsimonious Random Vector Functional Link Network for Data Streams—0
Recognizing Mentions of Adverse Drug Reaction in Social Media Using Knowledge-Infused Recurrent Models—0
Don't Stop Me Now! Using Global Dynamic Oracles to Correct Training Biases of Transition-Based Dependency Parsers—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
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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