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 901–950 of 3073 papers

TitleStatusHype
BASIL: Balanced Active Semi-supervised Learning for Class Imbalanced Datasets—0
Active Learning for Dependency Parsing with Partial Annotation—0
Batch Active Learning from the Perspective of Sparse Approximation—0
Batch Active Learning of Reward Functions from Human Preferences—0
Active emulation of computer codes with Gaussian processes -- Application to remote sensing—0
Batch Active Learning via Coordinated Matching—0
AI-Enhanced Data Processing and Discovery Crowd Sourcing for Meteor Shower Mapping—0
Active learning for detection of stance components—0
Active Learning with Statistical Models—0
Computer-assisted Speaker Diarization: How to Evaluate Human Corrections—0
Batch Multi-Fidelity Active Learning with Budget Constraints—0
Batch versus Sequential Active Learning for Recommender Systems—0
BayesFormer: Transformer with Uncertainty Estimation—0
Bayesian Active Edge Evaluation on Expensive Graphs—0
Bayesian Active Learning by Disagreements: A Geometric Perspective—0
Active Learning with TensorBoard Projector—0
Bayesian Active Learning for Censored Regression—0
Bayesian active learning for choice models with deep Gaussian processes—0
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
Bayesian Active Learning for Discrete Latent Variable Models—0
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—0
Active learning with version spaces for object detection—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
Bayesian Active Learning for Scanning Probe Microscopy: from Gaussian Processes to Hypothesis Learning—0
Bayesian Active Learning for Structured Output Design—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Bayesian Active Learning of (small) Quantile Sets through Expected Estimator Modification—0
Bayesian Active Learning With Abstention Feedbacks—0
Active Learning for Structured Prediction from Partially Labeled Data—0
Bayesian active learning with localized priors for fast receptive field characterization—0
Active Learning Approach to Optimization of Experimental Control—0
Bayesian Active Summarization—0
Actively learning a Bayesian matrix fusion model with deep side information—0
Bayesian Bias Mitigation for Crowdsourcing—0
Actively Learning Combinatorial Optimization Using a Membership Oracle—0
Active feature selection discovers minimal gene sets for classifying cell types and disease states with single-cell mRNA-seq data—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Actively Learning Concepts and Conjunctive Queries under ELr-Ontologies—0
Bayesian Estimate of Mean Proper Scores for Diversity-Enhanced Active Learning—0
Bayesian Experimental Design for Finding Reliable Level Set under Input Uncertainty—0
Federated Learning with Uncertainty via Distilled Predictive Distributions—0
Bayesian Generative Active Deep Learning—0
Bayesian Hypernetworks—0
A Graph-Based Approach for Active Learning in Regression—0
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Actively Learning Hemimetrics with Applications to Eliciting User Preferences—0
Bayesian Nonparametric Crowdsourcing—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
Agnostic Multi-Group 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