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 1176–1200 of 3073 papers

TitleStatusHype
CFlowNets: Continuous Control with Generative Flow NetworksCode0
BenchDirect: A Directed Language Model for Compiler Benchmarks—0
Active Learning Enhances Classification of Histopathology Whole Slide Images with Attention-based Multiple Instance Learning—0
Active Learning and Bayesian Optimization: a Unified Perspective to Learn with a Goal—0
Containing a spread through sequential learning: to exploit or to explore?—0
Implementing Active Learning in Cybersecurity: Detecting Anomalies in Redacted Emails—0
Active Learning with Combinatorial Coverage—0
A Survey on Uncertainty Quantification Methods for Deep Learning—0
Deep active learning for nonlinear system identification—0
Active learning for structural reliability analysis with multiple limit state functions through variance-enhanced PC-Kriging surrogate models—0
Deep Active Learning in the Presence of Label Noise: A Survey—0
Evaluating the effect of data augmentation and BALD heuristics on distillation of Semantic-KITTI dataset—0
Correlation Clustering with Active Learning of Pairwise Similarities—0
Black-Box Batch Active Learning for RegressionCode0
Active learning for data streams: a survey—0
Gaussian Switch Sampling: A Second Order Approach to Active LearningCode0
Robust expected improvement for Bayesian optimization—0
Adaptive Selective Sampling for Online Prediction with Experts—0
Navya3DSeg -- Navya 3D Semantic Segmentation Dataset & split generation for autonomous vehicles—0
ScatterShot: Interactive In-context Example Curation for Text TransformationCode0
Investigating Multi-source Active Learning for Natural Language InferenceCode0
Algorithm Selection for Deep Active Learning with Imbalanced DatasetsCode0
Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play—0
CRL+: A Novel Semi-Supervised Deep Active Contrastive Representation Learning-Based Text Classification Model for Insurance Data—0
Best Practices in Active Learning for Semantic Segmentation—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