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 2001–2050 of 3073 papers

TitleStatusHype
Wireless for Machine Learning—0
A Survey of Deep Active LearningCode0
Cost-Quality Adaptive Active Learning for Chinese Clinical Named Entity Recognition—0
Buy Me That Look: An Approach for Recommending Similar Fashion ProductsCode0
Bayesian Force Fields from Active Learning for Simulation of Inter-Dimensional Transformation of StaneneCode1
Mask-guided sample selection for Semi-Supervised Instance Segmentation—0
Deep Active Learning in Remote Sensing for data efficient Change DetectionCode1
Active learning of deep surrogates for PDEs: Application to metasurface design—0
Probabilistic Deep Learning for Instance Segmentation—0
What am I allowed to do here?: Online Learning of Context-Specific Norms by Pepper—0
Graudally Applying Weakly Supervised and Active Learning for Mass Detection in Breast Ultrasound ImagesCode0
Analysis of Social Robotic Navigation approaches: CNN Encoder and Incremental Learning as an alternative to Deep Reinforcement Learning—0
A Survey of Active Learning for Text Classification using Deep Neural Networks—0
A New Perspective on Pool-Based Active Classification and False-Discovery Control—0
Contextual Diversity for Active LearningCode1
Iterative Surrogate Model Optimization (ISMO): An active learning algorithm for PDE constrained optimization with deep neural networksCode0
Online Graph Completion: Multivariate Signal Recovery in Computer Vision—0
DeepDrummer : Generating Drum Loops using Deep Learning and a Human in the LoopCode1
Deep Active Learning with Crowdsourcing Data for Privacy Policy Classification—0
Cross-Model Image Annotation Platform with Active Learning—0
Importance of Self-Consistency in Active Learning for Semantic Segmentation—0
Active Classification with Uncertainty Comparison QueriesCode0
Dual Adversarial Network for Deep Active Learning—0
Weight Decay Scheduling and Knowledge Distillation for Active Learning—0
Two Stream Active Query Suggestion for Active Learning in Connectomics—0
Cross-context News Corpus for Protest Events related Knowledge Base ConstructionCode0
Learning to Rank for Active Learning: A Listwise Approach—0
Is there something I'm missing? Topic Modeling in eDiscovery—0
On Deep Unsupervised Active Learning—0
Active Learning for Video Description With Cluster-Regularized Ensemble Ranking—0
Fast active learning for pure exploration in reinforcement learning—0
Deep Active Learning for Solvability Prediction in Power Systems—0
Deep Active Learning by Model Interpretability—0
MetAL: Active Semi-Supervised Learning on Graphs via Meta LearningCode0
DEAL: Deep Evidential Active Learning for Image ClassificationCode1
Efficient Graph-Based Active Learning with Probit Likelihood via Gaussian Approximations—0
Toward Machine-Guided, Human-Initiated Explanatory Interactive Learning—0
Advances in Deep Learning for Hyperspectral Image Analysis--Addressing Challenges Arising in Practical Imaging Scenarios—0
Active Learning under Label Shift—0
Active Crowd Counting with Limited Supervision—0
On uncertainty estimation in active learning for image segmentationCode1
IALE: Imitating Active Learner EnsemblesCode0
Resource Aware Multifidelity Active Learning for Efficient Optimization—0
Active Learning on Attributed Graphs via Graph Cognizant Logistic Regression and Preemptive Query Generation—0
Few Is Enough: Task-Augmented Active Meta-Learning for Brain Cell Classification—0
A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT ImagesCode0
ASGN: An Active Semi-supervised Graph Neural Network for Molecular Property PredictionCode1
Meta-active Learning in Probabilistically-Safe Optimization—0
The Sample Complexity of Best-k Items Selection from Pairwise ComparisonsCode0
Linear Bandits with Limited Adaptivity and Learning Distributional Optimal Design—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