A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection-Reference-Cited by-同舟云学术

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A Continual Learning System with Self Domain Shift Adaptation for Fake News Detection

Published:2023-10-09 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE 10th International Conference on Data Science and Advanced Analytics (DSAA)
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Author:

Basterrech Sebastián¹,Kasprzak Andrzej²,Platoš Jan³,Woźniak Michał²

Affiliation:

1. Technical University of Denmark,Department of Applied Mathematics and Computer Science,Kongens Lyngby,Denmark

2. Wroclaw University of Science and Technology,Department of Systems and Computer Networks,Wroclaw,Poland

3. VŠB-Technical University of Ostrava,Faculty of Electrical Engineering and Computer Science,Ostrava,Czech Republic

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10302431/10302461/10302539.pdf?arnumber=10302539

Reference34 articles.

1. Gradient episodic memory for continuum learning;lopez-paz;CoRR,2017

2. iCaRL: Incremental Classifier and Representation Learning

3. Sequential Model-Based Optimization for General Algorithm Configuration

4. Survey of distance measures for quantifying concept drift and shift in numeric data

5. Progressive neural networks;rusu;CoRR,2016

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1. A Self-Organizing Clustering System for Unsupervised Distribution Shift Detection;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. Application of Instrumented Indentation Test and Neural Networks to determine the constitutive model of in-situ austenitic stainless steel components;Archives of Civil and Mechanical Engineering;2024-04-22

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