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 1951–2000 of 3073 papers

TitleStatusHype
Cost-Based Budget Active Learning for Deep Learning—0
Active Learning: Problem Settings and Recent Developments—0
A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design—0
Perfect density models cannot guarantee anomaly detection—0
Active Learning Methods for Efficient Hybrid Biophysical Variable Retrieval—0
Bayesian Active Learning for Wearable Stress and Affect Detection—0
Fine-tuning BERT for Low-Resource Natural Language Understanding via Active Learning—0
Sparse Semi-Supervised Action Recognition with Active Learning—0
Stochastic Adversarial Gradient Embedding for Active Domain Adaptation—0
Message Passing Adaptive Resonance Theory for Online Active Semi-supervised Learning—0
Deep Multi-Fidelity Active Learning of High-dimensional Outputs—0
High-contrast “gaudy” images improve the training of deep neural network models of visual cortexCode0
CORA: A Deep Active Learning Covid-19 Relevancy Algorithm to Identify Core Scientific Articles—0
Constructing a Korean Named Entity Recognition Dataset for the Financial Domain using Active Learning—0
Bilingual Transfer Learning for Online Product Classification—0
Uncertainty Modeling for Machine Comprehension Systems using Efficient Bayesian Neural Networks—0
A Multitask Active Learning Framework for Natural Language Understanding—0
Enhanced Labelling in Active Learning for Coreference Resolution—0
Variance based sensitivity analysis for Monte Carlo and importance sampling reliability assessment with Gaussian processes—0
On Initial Pools for Deep Active LearningCode0
Active Output Selection Strategies for Multiple Learning Regression Models—0
A smartphone based multi input workflow for non-invasive estimation of haemoglobin levels using machine learning techniques—0
Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient—0
Active Learning in CNNs via Expected Improvement MaximizationCode0
Low-Resolution Face Recognition In Resource-Constrained Environments—0
Cost-effective Variational Active Entity Resolution—0
Finding the Homology of Decision Boundaries with Active LearningCode0
SAFARI: Safe and Active Robot Imitation Learning with Imagination—0
Augmented Fairness: An Interpretable Model Augmenting Decision-Makers' Fairness—0
A Transfer Learning Based Active Learning Framework for Brain Tumor Classification—0
Sampling Approach Matters: Active Learning for Robotic Language Acquisition—0
On the Marginal Benefit of Active Learning: Does Self-Supervision Eat Its Cake?—0
Medical symptom recognition from patient text: An active learning approach for long-tailed multilabel distributions—0
Active Learning from Crowd in Document Screening—0
LADA: Look-Ahead Data Acquisition via Augmentation for Active Learning—0
Action State Update Approach to Dialogue Management—0
ROIAL: Region of Interest Active Learning for Characterizing Exoskeleton Gait Preference LandscapesCode0
Human-Like Active Learning: Machines Simulating the Human Learning Process—0
Revisiting Model-Agnostic Private Learning: Faster Rates and Active Learning—0
Beyond Marginal Uncertainty: How Accurately can Bayesian Regression Models Estimate Posterior Predictive Correlations?Code0
Deep Active Learning with Augmentation-based Consistency EstimationCode0
Participation in TREC 2020 COVID Track Using Continuous Active Learning—0
Exemplar Guided Active Learning—0
Reducing Confusion in Active Learning for Part-Of-Speech Tagging—0
Multi-Modal Active Learning for Automatic Liver Fibrosis Diagnosis based on Ultrasound Shear Wave Elastography—0
Textual Data Augmentation for Efficient Active Learning on Tiny Datasets—0
Uncertainty and Traffic-Aware Active Learning for Semantic Parsing—0
Active Learning Approaches to Enhancing Neural Machine Translation—0
A Smart System to Generate and Validate Question Answer Pairs for COVID-19 Literature—0
Learning Structured Representations of Entity Names using Active Learning and Weak SupervisionCode0
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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