Learning Sparse Alignments via Optimal Transport for Cross-Domain Fake News Detection

Author:

Tang Wei1,Ma Zuyao1,Sun Haifeng1,Wang Jingyu1

Affiliation:

1. Beijing University of Posts and Telecommunications,China

Funder

National Natural Science Foundation of China

Ministry of Education

Beijing University of Posts and Telecommunications

China Mobile Research Institute

Publisher

IEEE

Reference21 articles.

1. Decoupled certainty-driven consistency loss for semi-supervised learning;liu,2019

2. Margin-aware adversarial domain adaptation with optimal transport;dhouib;International Conference on Machine Learning,2020

3. spacy 2: Natural language understanding with bloom embeddings, convolutional neural networks and incremental parsing;honnibal;To appear,2017

4. Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results;tarvainen;Proceedings of the 31st International Conference on Neural Information Processing Systems,2017

5. Fast graph representation learning with PyTorch Geometric;fey;ICLR Workshop on Representation Learning on Graphs and Manifolds,2019

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Detecção de Fake News em Domínios Cruzados: Uma Revisão Sistemática;Anais do XIII Brazilian Workshop on Social Network Analysis and Mining (BraSNAM 2024);2024-07-21

2. Fake News Detection Using Deep Learning: A Systematic Literature Review;IEEE Access;2024

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