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 2301–2350 of 3073 papers

TitleStatusHype
Deep Reinforcement Active Learning for Human-in-the-Loop Person Re-Identification—0
Active Anomaly Detection for time-domain discoveries—0
Noisy Batch Active Learning with Deterministic AnnealingCode0
Data-driven discovery of free-form governing differential equations—0
Active Learning for Event Detection in Support of Disaster Analysis Applications—0
Consistency-Based Semi-Supervised Active Learning: Towards Minimizing Labeling Budget—0
Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets—0
Training Data Distribution Search with Ensemble Active Learning—0
Transfer Active Learning For Graph Neural Networks—0
Active Learning Graph Neural Networks via Node Feature Propagation—0
Gaussian Process Meta-Representations Of Neural Networks—0
Learning in Confusion: Batch Active Learning with Noisy Oracle—0
Sampling Bias in Deep Active Classification: An Empirical StudyCode0
Active Learning for Risk-Sensitive Inverse Reinforcement Learning—0
Active learning for level set estimation under cost-dependent input uncertainty—0
Towards Generalizable Deepfake Detection with Locality-aware AutoEncoder—0
On weighted uncertainty sampling in active learning—0
A Flexible Framework for Anomaly Detection via Dimensionality ReductionCode0
Learning to Sample: an Active Learning Framework—0
Active learning to optimise time-expensive algorithm selection—0
An Active Learning Approach for Reducing Annotation Cost in Skin Lesion AnalysisCode0
Augmented Memory Networks for Streaming-Based Active One-Shot Learning—0
Active Collaborative Sensing for Energy BreakdownCode0
On the Expressiveness of Approximate Inference in Bayesian Neural NetworksCode0
Turning silver into gold: error-focused corpus reannotation with active learning—0
Active Learning for Financial Investment Reports—0
Epistemic Uncertainty Sampling—0
Temporal Coherence for Active Learning in Videos—0
Active Learning for Domain Classification in a Commercial Spoken Personal Assistant—0
O-MedAL: Online Active Deep Learning for Medical Image AnalysisCode0
A novel active learning-based Gaussian process metamodelling strategy for estimating the full probability distribution in forward UQ analysis—0
A Little Annotation does a Lot of Good: A Study in Bootstrapping Low-resource Named Entity RecognizersCode0
SEAL: Semi-supervised Adversarial Active Learning on Attributed Graphs—0
NE-LP: Normalized Entropy and Loss Prediction based Sampling for Active Learning in Chinese Word Segmentation on EHRs—0
ED2: Two-stage Active Learning for Error Detection -- Technical Report—0
Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian ModelCode0
Supervised Negative Binomial Classifier for Probabilistic Record Linkage—0
An Adaptive Supervision Framework for Active Learning in Object Detection—0
Bayesian Batch Active Learning as Sparse Subset ApproximationCode0
Automatic Playtesting for Game Parameter Tuning via Active Learning—0
A Survey on Deep Learning of Small Sample in Biomedical Image AnalysisCode0
Incremental Domain Adaptation for Neural Machine Translation in Low-Resource SettingsCode0
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
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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