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 1851–1900 of 3073 papers

TitleStatusHype
Active Learning Under Malicious Mislabeling and Poisoning Attacks—0
SoCal: Selective Oracle Questioning for Consistency-based Active Learning of Physiological Signals—0
Efficiently labelling sequences using semi-supervised active learning—0
Deep Active Learning for Object Detection with Mixture Density Networks—0
Better Optimization can Reduce Sample Complexity: Active Semi-Supervised Learning via Convergence Rate Control—0
Learning Active Learning in the Batch-Mode Setup with Ensembles of Active Learning Agents—0
Machine Learning Algorithms for Data Labeling: An Empirical Evaluation—0
On the Geometry of Deep Bayesian Active Learning—0
Least Probable Disagreement Region for Active Learning—0
Learning to Make Decisions via Submodular Regularization—0
Uncertainty-aware Active Learning for Optimal Bayesian Classifier—0
Towards Understanding the Behaviors of Optimal Deep Active Learning AlgorithmsCode1
From Handheld to Unconstrained Object Detection: a Weakly-supervised On-line Learning Approach—0
Whom to Test? Active Sampling Strategies for Managing COVID-19—0
An Active Learning Method for Diabetic Retinopathy Classification with Uncertainty Quantification—0
Active Deep Learning on Entity Resolution by Risk Sampling—0
Self-supervised self-supervision by combining deep learning and probabilistic logic—0
Learning Halfspaces With Membership Queries—0
Multi-fidelity Bayesian Neural Networks: Algorithms and Applications—0
On the Power of Localized Perceptron for Label-Optimal Learning of Halfspaces with Adversarial Noise—0
An Information-Theoretic Framework for Unifying Active Learning ProblemsCode0
GLISTER: Generalization based Data Subset Selection for Efficient and Robust LearningCode1
Rebuilding Trust in Active Learning with Actionable Metrics—0
Minimax Active Learning—0
Embodied Visual Active Learning for Semantic Segmentation—0
Learning active learning at the crossroads? evaluation and discussion—0
Active Learning for Deep Gaussian Process SurrogatesCode0
Chernoff Sampling for Active Testing and Extension to Active Regression—0
Deep Bayesian Active Learning, A Brief Survey on Recent Advances—0
CODE: Contrastive Pre-training with Adversarial Fine-tuning for Zero-shot Expert LinkingCode1
Active Hierarchical Imitation and Reinforcement Learning—0
LSCALE: Latent Space Clustering-Based Active Learning for Node ClassificationCode0
Accelerating high-throughput virtual screening through molecular pool-based active learningCode1
A unified framework for closed-form nonparametric regression, classification, preference and mixed problems with Skew Gaussian ProcessesCode1
Semi-supervised Active Learning for Instance Segmentation via Scoring Predictions—0
Cost-Based Budget Active Learning for Deep Learning—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Active Learning: Problem Settings and Recent Developments—0
Enhanced spatio-temporal electric load forecasts using less data with active deep learningCode1
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Perfect density models cannot guarantee anomaly detection—0
What Makes a "Good" Data Augmentation in Knowledge Distillation -- A Statistical PerspectiveCode1
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Enhanced Labelling in Active Learning for Coreference Resolution—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