Summary of Interpretable Tensor Fusion, by Saurabh Varshneya et al.
Interpretable Tensor Fusion
by Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
First submitted to arxiv on: 7 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel multimodal learning method called Interpretable Tensor Fusion (InTense) is introduced, which enables neural networks to simultaneously learn representations from diverse data types, such as text, images, and audio. InTense separates linear combinations and multiplicative interactions between modalities, providing interpretability by assigning relevance scores. This approach outperforms existing state-of-the-art multimodal interpretable methods on six real-world datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary InTense is a new way to learn from different types of data, like words, pictures, and sounds. It helps computers understand how these different types of data are related and what each one contributes to the overall meaning. This approach gives insights into why certain things happen by showing which parts of the data are most important. |
