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 2326–2350 of 3073 papers

TitleStatusHype
FDive: Learning Relevance Models using Pattern-based Similarity Measures—0
Mindful Active LearningCode0
Automated Muscle Segmentation from Clinical CT using Bayesian U-Net for Personalized Musculoskeletal ModelingCode0
Photonic architecture for reinforcement learning—0
Half a Percent of Labels is Enough: Efficient Animal Detection in UAV Imagery using Deep CNNs and Active Learning—0
Output-weighted optimal sampling for Bayesian regression and rare event statistics using few samples—0
Modeling Human Annotation Errors to Design Bias-Aware Systems for Social Stream Processing—0
MedCATTrainer: A Biomedical Free Text Annotation Interface with Active Learning and Research Use Case Specific Customisation—0
Discriminative Active LearningCode0
Self-Regulated Interactive Sequence-to-Sequence LearningCode0
Deep Active Learning for Axon-Myelin Segmentation on Histology DataCode1
The Power of Comparisons for Actively Learning Linear Classifiers—0
A Semi-Supervised Framework for Automatic Pixel-Wise Breast Cancer Grading of Histological Images—0
AlpacaTag: An Active Learning-based Crowd Annotation Framework for Sequence Tagging—0
Learning How to Active Learn by DreamingCode0
Active Learning within Constrained Environments through Imitation of an Expert Questioner—0
The Practical Challenges of Active Learning: Lessons Learned from Live Experimentation—0
L*-Based Learning of Markov Decision Processes (Extended Version)—0
'In-Between' Uncertainty in Bayesian Neural Networks—0
Deep Active Learning with Adaptive AcquisitionCode0
Selection via Proxy: Efficient Data Selection for Deep LearningCode0
A Tight Analysis of Greedy Yields Subexponential Time Approximation for Uniform Decision Tree—0
Active Learning Solution on Distributed Edge Computing—0
Confidence Calibration for Convolutional Neural Networks Using Structured Dropout—0
Flattening a Hierarchical Clustering through 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