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 101–125 of 3073 papers

TitleStatusHype
A comprehensive survey on deep active learning in medical image analysisCode1
A Simple Baseline for Low-Budget Active LearningCode1
A Survey of Dataset Refinement for Problems in Computer Vision DatasetsCode1
AcTune: Uncertainty-aware Active Self-Training for Semi-Supervised Active Learning with Pretrained Language ModelsCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Bayesian Active Learning with Fully Bayesian Gaussian ProcessesCode1
On the Importance of Effectively Adapting Pretrained Language Models for Active LearningCode1
Active Invariant Causal Prediction: Experiment Selection through StabilityCode1
Bayesian Optimization with Conformal Prediction SetsCode1
Active Anomaly Detection via EnsemblesCode1
BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active AnnotationCode1
Building a Scalable and Interpretable Bayesian Deep Learning Framework for Quality Control of Free Form SurfacesCode1
Can Active Learning Preemptively Mitigate Fairness Issues?Code1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
ChemSpaceAL: An Efficient Active Learning Methodology Applied to Protein-Specific Molecular GenerationCode1
CitySurfaces: City-Scale Semantic Segmentation of Sidewalk MaterialsCode1
Class-Balanced Active Learning for Image ClassificationCode1
A Benchmark on Uncertainty Quantification for Deep Learning PrognosticsCode1
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
Consistency-based Active Learning for Object DetectionCode1
Active Learning for Open-set AnnotationCode1
Active Learning for Optimal Intervention Design in Causal ModelsCode1
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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