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 1201–1250 of 3073 papers

TitleStatusHype
Best Practices in Active Learning for Semantic Segmentation—0
AutoWS: Automated Weak Supervision Framework for Text Classification—0
Active Learning in Brain Tumor Segmentation with Uncertainty Sampling, Annotation Redundancy Restriction, and Data Initialization—0
Normalizing Flow Ensembles for Rich Aleatoric and Epistemic Uncertainty ModelingCode0
A Semi-Parametric Model for Decision Making in High-Dimensional Sensory Discrimination Tasks—0
Robust online active learning—0
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement LearningCode0
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion—0
Does Deep Active Learning Work in the Wild?—0
Identifying Adversarially Attackable and Robust SamplesCode0
Active Learning for Multilingual Semantic Parser—0
Leveraging Importance Weights in Subset Selection—0
Dual Control of Exploration and Exploitation for Auto-Optimisation Control with Active Learning—0
ActiveLab: Active Learning with Re-Labeling by Multiple Annotators—0
HAL3D: Hierarchical Active Learning for Fine-Grained 3D Part Labeling—0
Semi-Automated Construction of Food Composition Knowledge BaseCode0
Cross-lingual German Biomedical Information Extraction: from Zero-shot to Human-in-the-Loop—0
Speeding Up BatchBALD: A k-BALD Family of Approximations for Active Learning—0
Active Learning of Piecewise Gaussian Process Surrogates—0
Sample-Efficient Multi-Objective Learning via Generalized Policy Improvement PrioritizationCode0
TAAL: Test-time Augmentation for Active Learning in Medical Image SegmentationCode0
Computational Assessment of Hyperpartisanship in News TitlesCode0
Scalable Batch Acquisition for Deep Bayesian Active LearningCode0
Forgetful Active Learning with Switch Events: Efficient Sampling for Out-of-Distribution Data—0
Combining Self-labeling with Selective Sampling—0
Active Learning for Abstractive Text SummarizationCode0
A domain-decomposed VAE method for Bayesian inverse problems—0
Active Learning Guided by Efficient Surrogate Learners—0
How to Allocate your Label Budget? Choosing between Active Learning and Learning to Reject in Anomaly DetectionCode0
Discovery of structure-property relations for molecules via hypothesis-driven active learning over the chemical space—0
An interpretable machine learning system for colorectal cancer diagnosis from pathology slides—0
Benchmarks and Algorithms for Offline Preference-Based Reward Learning—0
Using Active Learning Methods to Strategically Select Essays for Automated Scoring—0
MHPL: Minimum Happy Points Learning for Active Source Free Domain Adaptation—0
Hybrid Active Learning via Deep Clustering for Video Action Detection—0
Deep Deterministic Uncertainty: A New Simple Baseline—0
Heterogeneous Diversity Driven Active Learning for Multi-Object Tracking—0
DiRaC-I: Identifying Diverse and Rare Training Classes for Zero-Shot Learning—0
Label-Efficient Interactive Time-Series Anomaly Detection—0
Deep Active Learning Using Barlow Twins—0
Active Learning for Neural Machine TranslationCode0
Adversarial Virtual Exemplar Learning for Label-Frugal Satellite Image Change Detection—0
Curator: Creating Large-Scale Curated Labelled Datasets using Self-Supervised Learning—0
Gaussian Process Classification Bandits—0
Online Active Learning for Soft Sensor Development using Semi-Supervised Autoencoders—0
An active learning method for solving competitive multi-agent decision-making and control problemsCode0
Towards Rapid Prototyping and Comparability in Active Learning for Deep Object Detection—0
Temporal Output Discrepancy for Loss Estimation-based Active Learning—0
Variational Factorization Machines for Preference Elicitation in Large-Scale Recommender SystemsCode0
Smooth Sailing: Improving Active Learning for Pre-trained Language Models with Representation Smoothness Analysis—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