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 951–1000 of 3073 papers

TitleStatusHype
Active Learning for Structured Probabilistic Models With Histogram Approximation—0
AI-based automated active learning for discovery of hidden dynamic processes: A use case in light microscopy—0
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning—0
Bayesian Semisupervised Learning with Deep Generative Models—0
Active metric learning and classification using similarity queries—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
Active Learning for Structured Prediction from Partially Labeled Data—0
Active Learning Approach to Optimization of Experimental Control—0
Benchmarking Active Learning Strategies for Materials Optimization and Discovery—0
Benchmarking Multi-Domain Active Learning on Image Classification—0
Active Learning for Event Extraction with Memory-based Loss Prediction Model—0
A Histopathology Study Comparing Contrastive Semi-Supervised and Fully Supervised Learning—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
A Graph-Based Approach for Active Learning in Regression—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
Active Nearest-Neighbor Learning in Metric Spaces—0
Active Learning for Financial Investment Reports—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—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
Active operator learning with predictive uncertainty quantification for partial differential equations—0
Big Batch Bayesian Active Learning by Considering Predictive Probabilities—0
Agnostic Multi-Group Active Learning—0
Bilingual Active Learning for Relation Classification via Pseudo Parallel Corpora—0
Bilingual Transfer Learning for Online Product Classification—0
Agnostic Active Learning Without Constraints—0
Active Perceptual Similarity Modeling with Auxiliary Information—0
Active Learning for Gaussian Process Considering Uncertainties with Application to Shape Control of Composite Fuselage—0
Active Learning for Speech Recognition: the Power of Gradients—0
Boosting API Recommendation with Implicit Feedback—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
Boosting Semi-Supervised Object Detection in Remote Sensing Images With Active Teaching—0
Active Bird2Vec: Towards End-to-End Bird Sound Monitoring with Transformers—0
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation—0
Bootstrapping Phrase-based Statistical Machine Translation via WSD Integration—0
Boundary Matters: A Bi-Level Active Finetuning Framework—0
Bounded Memory Active Learning through Enriched Queries—0
Bounds on the Generalization Error in Active Learning—0
Agnostic Active Learning of Single Index Models with Linear Sample Complexity—0
Aggregation Strategies for Efficient Annotation of Bioacoustic Sound Events Using 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