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 2851–2900 of 3073 papers

TitleStatusHype
Bayesian Active Learning for Multi-Criteria Comparative Judgement in Educational Assessment—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
Bayesian active learning with localized priors for fast receptive field characterization—0
Bayesian Active Summarization—0
Bayesian Bias Mitigation for Crowdsourcing—0
Towards Bayesian Data Selection—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
Bayesian multi-objective optimization for stochastic simulators: an extension of the Pareto Active Learning method—0
Bayesian Nonparametric Crowdsourcing—0
Bayesian optimization for robust robotic grasping using a sensorized compliant hand—0
Bayesian Pool-based Active Learning With Abstention Feedbacks—0
Bayesian Quadrature Optimization for Probability Threshold Robustness Measure—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Bayesian Semisupervised Learning with Deep Generative Models—0
BayesOpt: A Library for Bayesian optimization with Robotics Applications—0
Bayes-Optimal Entropy Pursuit for Active Choice-Based Preference Learning—0
Beating the Minimax Rate of Active Learning with Prior Knowledge—0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
Benchmarking Active Learning for NILM—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
BERT-PersNER: A New Model for Persian Named Entity Recognition—0
Best Arm Identification for Contaminated Bandits—0
Best Practices in Pool-based Active Learning for Image Classification—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Beyond Accuracy: ROI-driven Data Analytics of Empirical Data—0
Beyond Active Learning: Leveraging the Full Potential of Human Interaction via Auto-Labeling, Human Correction, and Human Verification—0
Beyond Comparing Image Pairs: Setwise Active Learning for Relative Attributes—0
Beyond Disagreement-based Agnostic Active Learning—0
Beyond Simple Meta-Learning: Multi-Purpose Models for Multi-Domain, Active and Continual Few-Shot Learning—0
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection—0
Bias-Aware Heapified Policy for Active Learning—0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry—0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
BI-LAVA: Biocuration with Hierarchical Image Labeling through Active Learning and Visual Analysis—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Bilingual Transfer Learning for Online Product Classification—0
Boosting API Recommendation with Implicit Feedback—0
Boosting Robustness Verification of Semantic Feature Neighborhoods—0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Boosting the Learning for Ranking Patterns—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