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 1151–1200 of 3073 papers

TitleStatusHype
Fairness-Aware Data Valuation for Supervised Learning—0
Automated wildlife image classification: An active learning tool for ecological applicationsCode0
Assorted, Archetypal and Annotated Two Million (3A2M) Cooking Recipes Dataset based on Active Learning—0
Active Self-Supervised Learning: A Few Low-Cost Relationships Are All You Need—0
Deep Active Learning with Contrastive Learning Under Realistic Data Pool Assumptions—0
Deep Kernel Methods Learn Better: From Cards to Process Optimization—0
Uncertainty Aware Active Learning for Reconfiguration of Pre-trained Deep Object-Detection Networks for New Target Domains—0
Agave crop segmentation and maturity classification with deep learning data-centric strategies using very high-resolution satellite imagery—0
Active Coarse-to-Fine Segmentation of Moveable Parts from Real Images—0
A general-purpose AI assistant embedded in an open-source radiology information system—0
Active Learning-based Model Predictive Coverage Control—0
Stochastic Submodular Maximization via Polynomial Estimators—0
Hierarchical-Hyperplane Kernels for Actively Learning Gaussian Process Models of Nonstationary SystemsCode0
LRDB: LSTM Raw data DNA Base-caller based on long-short term models in an active learning environment—0
Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency—0
Bi-directional personalization reinforcement learning-based architecture with active learning using a multi-model data service for the travel nursing industry—0
Statistical Hardware Design With Multi-model Active Learning—0
Bi3D: Bi-domain Active Learning for Cross-domain 3D Object Detection—0
Fast post-process Bayesian inference with Variational Sparse Bayesian QuadratureCode0
Active Learning Based Domain Adaptation for Tissue Segmentation of Histopathological Images—0
Privacy-preserving and Uncertainty-aware Federated Trajectory Prediction for Connected Autonomous Vehicles—0
Embodied Active Learning of Relational State Abstractions for Bilevel Planning—0
Disambiguation of Company names via Deep Recurrent NetworksCode0
Face: Fast, Accurate and Context-Aware Audio Annotation and ClassificationCode0
Active learning using region-based sampling—0
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