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Vicarious Learning and Multimodal Dialogue

Sixth IEEE International Conference on Advanced Learning Technologies (ICALT'06), 2006
Vicarious Learning is learning from watching others learn. We believe that this is a powerful model for computer-based learning. Learning episodes can be captured and replayed to later learners: this depends on the learning content being accessible and as far as possible external.
openaire   +2 more sources

Pan-cancer integrative histology-genomic analysis via multimodal deep learning

Cancer Cell, 2022
Faisal Mahmood   +2 more
exaly  

Multimodal Co-learning: Challenges, applications with datasets, recent advances and future directions

Information Fusion, 2022
Rahee Walambe   +2 more
exaly  

Symbiosis in Multimodal Concept Learning

1995
In this paper we discuss an approach for learning multimodal concepts with Genetic Algorithms. The presented system Join-GA is a Genetic Algorithm for first order logic concept learning that has some features from both Michigan and Pittsburgh approaches.
openaire   +2 more sources

An overview of deep learning methods for multimodal medical data mining

Expert Systems With Applications, 2022
Mohammad Saniee Abadeh
exaly  

Backdooring Multimodal Learning

2024 IEEE Symposium on Security and Privacy (SP)
Xingshuo Han   +7 more
openaire   +1 more source

Excavating multimodal correlation for representation learning

Information Fusion, 2023
Sijie Mai, Haifeng Hu, Ying Zeng
exaly  

Multimodal Data Efficient Learning

Recently, unimodal models have attained good performance in many tasks. However, using one modality may not provide sufficient information in complex situations. Humans use multimodal input, such as vision and hearing, to act in the real world. Similarly, this thesis proposes systems that use multimodal input for video classification and visual ...
openaire   +2 more sources

Multimodal Intelligence: Representation Learning, Information Fusion, and Applications

IEEE Journal on Selected Topics in Signal Processing, 2020
Chao Zhang, Xiaodong He
exaly  

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