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Deep active learning framework for chest-abdominal CT scans segmentation
State-of-the-art deep learning approaches for segmentation require large, high-quality
annotated datasets for training and evaluation. However, the manual processes for creating …
annotated datasets for training and evaluation. However, the manual processes for creating …
Deuce: Dual-diversity Enhancement and Uncertainty-awareness for Cold-start Active Learning
Cold-start active learning (CSAL) selects valuable instances from an unlabeled dataset for
manual annotation. It provides high-quality data at a low annotation cost for label-scarce text …
manual annotation. It provides high-quality data at a low annotation cost for label-scarce text …
Tracking and handling behavioral biases in active learning frameworks
The computational capabilities of AI engines integrated with human knowledge and
experience can help create intelligent human-in-the-loop (HITL) decision systems. Such …
experience can help create intelligent human-in-the-loop (HITL) decision systems. Such …
Active Foundational Models for Fault Diagnosis of Electrical Motors
Fault detection and diagnosis of electrical motors are of utmost importance in ensuring the
safe and reliable operation of several industrial systems. Detection and diagnosis of faults at …
safe and reliable operation of several industrial systems. Detection and diagnosis of faults at …
Impacts of behavioral biases on active learning strategies
Cyber-Physical-Human Systems (CPHS) interconnect humans, physical plants and cyber
infrastructure across space and time. Industrial processes, electromechanical systems …
infrastructure across space and time. Industrial processes, electromechanical systems …
An Active Regression Learning Method for Quality Evaluation of Molecular Dynamics Data
Y Huang, D Zhao, H Shen - 2024 4th International Conference …, 2024 - ieeexplore.ieee.org
Training deep potentials for molecular dynamics simulations via active learning methods is a
prevalent technique. However, a significant drawback of this method is its tendency to get …
prevalent technique. However, a significant drawback of this method is its tendency to get …
Active Learning for Node Classification using a Convex Optimization approach
The recent advancements related to big data analytics in the era of Industry 4.0 are fueled by
development and deployment of decision models based on neural network (NN) …
development and deployment of decision models based on neural network (NN) …
[PDF][PDF] Introduction to Computer Engineering: Evidence-Based Inclusive Teaching Practices
Z Zhou - 2023 - aquila.usm.edu
1. State Clear Learning Goals repeatedly, so students have a clear idea of where they are
going and what it will look like when they get there. This is a practice that creates …
going and what it will look like when they get there. This is a practice that creates …
[PDF][PDF] Investigating Core Set-based Active Learning for Text Classification
Y Brenning - downloads.webis.de
Following the exponential growth in the number of readily available documents and texts in
recent years, the need for efficient methods of classification has become increasingly …
recent years, the need for efficient methods of classification has become increasingly …
Feature Enhancement with Deep Generative Models in Deep Bayesian Active Learning
PE Duymuş - 2022 - search.proquest.com
Data-intensive models emerge as new advances in Deep Learning take place. However,
access to annotated datasets with many data points is not constantly prevalent. This …
access to annotated datasets with many data points is not constantly prevalent. This …