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 151–200 of 3073 papers

TitleStatusHype
Effectiveness of Tree-based Ensembles for Anomaly Discovery: Insights, Batch and Streaming Active LearningCode1
A Holistic Approach to Undesired Content Detection in the Real WorldCode1
Active Bayesian Causal InferenceCode1
Active Imitation Learning with Noisy GuidanceCode1
All you need are a few pixels: semantic segmentation with PixelPickCode1
ALPBench: A Benchmark for Active Learning Pipelines on Tabular DataCode1
Active Learning Strategies for Weakly-supervised Object DetectionCode1
Active Learning at the ImageNet ScaleCode1
Active Learning for Deep Object Detection via Probabilistic ModelingCode1
A Simple Baseline for Low-Budget Active LearningCode1
AstronomicAL: An interactive dashboard for visualisation, integration and classification of data using Active LearningCode1
Active learning based generative design for the discovery of wide bandgap materialsCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
A Tutorial on Thompson SamplingCode1
Bamboo: Building Mega-Scale Vision Dataset Continually with Human-Machine SynergyCode1
Bayesian active learning for production, a systematic study and a reusable libraryCode1
Active Learning for Coreference Resolution using Discrete AnnotationCode1
Bayesian Optimization with Conformal Prediction SetsCode1
BenchPress: A Deep Active Benchmark GeneratorCode1
Biological Sequence Design with GFlowNetsCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Active Learning by Feature MixingCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Active Learning Meets Optimized Item SelectionCode1
CheXpert++: Approximating the CheXpert labeler for Speed,Differentiability, and Probabilistic OutputCode1
Class-Balanced Active Learning for Image ClassificationCode1
Closing the Loop: Fast, Interactive Semi-Supervised Annotation With Queries on Features and InstancesCode1
A Comparative Survey of Deep Active LearningCode1
Code-free development and deployment of deep segmentation models for digital pathologyCode1
COLosSAL: A Benchmark for Cold-start Active Learning for 3D Medical Image SegmentationCode1
Conditioning Sparse Variational Gaussian Processes for Online Decision-makingCode1
A2-LINK: Recognizing Disguised Faces via Active Learning and Adversarial Noise based Inter-Domain KnowledgeCode1
Contextual Diversity for Active LearningCode1
Counting People by Estimating People FlowsCode1
Creating Custom Event Data Without Dictionaries: A Bag-of-TricksCode1
CTooth+: A Large-scale Dental Cone Beam Computed Tomography Dataset and Benchmark for Tooth Volume SegmentationCode1
Unsupervised Selective Labeling for More Effective Semi-Supervised LearningCode1
Active Learning Helps Pretrained Models Learn the Intended TaskCode1
Dataset Quantization with Active Learning based Adaptive SamplingCode1
Deep Active Learning for Joint Classification & Segmentation with Weak AnnotatorCode1
Deep Active Learning for Named Entity RecognitionCode1
Active Learning for Bayesian 3D Hand Pose EstimationCode1
Active Learning for BERT: An Empirical StudyCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Active Learning of Markov Decision Processes using Baum-Welch algorithm (Extended)Code1
Active Learning Through a Covering LensCode1
Deep Indexed Active Learning for Matching Heterogeneous Entity RepresentationsCode1
DIAL: Deep Interactive and Active Learning for Semantic Segmentation in Remote SensingCode1
Active Learning for Open-set AnnotationCode1
Show:102550
← PrevPage 4 of 62Next →

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