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 1776–1800 of 3073 papers

TitleStatusHype
A Practical & Unified Notation for Information-Theoretic Quantities in ML—0
Not All Labels Are Equal: Rationalizing The Labeling Costs for Training Object Detection—0
ArgFuse: A Weakly-Supervised Framework for Document-Level Event Argument AggregationCode0
Phrase-level Active Learning for Neural Machine Translation—0
Corruption Robust Active Learning—0
Active and Dynamic Beam Tracking UnderStochastic Mobility—0
Active Learning for Deep Neural Networks on Edge Devices—0
Transferable Query Selection for Active Domain Adaptation—0
Revisiting Superpixels for Active Learning in Semantic Segmentation With Realistic Annotation Costs—0
Heuristic Stopping Rules For Technology-Assisted Review—0
An Information Retrieval Approach to Building Datasets for Hate Speech DetectionCode0
A Survey on Semi-Supervised Learning for Delayed Partially Labelled Data Streams—0
Effective Evaluation of Deep Active Learning on Image Classification Tasks—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
Automatic Analysis of the Emotional Content of Speech in Daylong Child-Centered Recordings from a Neonatal Intensive Care Unit—0
Active Learning for Network Traffic Classification: A Technical Study—0
Semi-supervised Active Regression—0
Rare event estimation using stochastic spectral embedding—0
Weighted Sparse Subspace Representation: A Unified Framework for Subspace Clustering, Constrained Clustering, and Active LearningCode0
Targeted Active Learning for Bayesian Decision-Making—0
Coresets for Classification -- Simplified and Strengthened—0
A critical look at the current train/test split in machine learning—0
Continual Active Learning for Efficient Adaptation of Machine Learning Models to Changing Image Acquisition—0
Neural Active Learning with Performance Guarantees—0
Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach—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