Summary of Mixmas: a Framework For Sampling-based Mixer Architecture Search For Multimodal Fusion and Learning, by Abdelmadjid Chergui et al.
MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
by Abdelmadjid Chergui, Grigor Bezirganyan, Sana Sellami, Laure Berti-Équille, Sébastien Fournier
First submitted to arxiv on: 24 Dec 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 The proposed framework, MixMAS, is a novel approach for searching the optimal deep learning architecture for multimodal data fusion tasks. This framework utilizes a sampling-based strategy to automatically select the most suitable MLP-based architecture for a given multimodal machine learning (MML) task. The approach explores various combinations of modality-specific encoders, fusion functions, and fusion networks, identifying the best-performing architecture based on performance metrics. MixMAS is particularly effective in handling diverse data types with distinct structures and characteristics. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MixMAS is a new way to find the right deep learning model for combining different kinds of data. This approach helps machines learn from lots of different types of information by automatically choosing the best way to combine them. It does this by trying out different combinations of ways to process each type of data, and then picks the one that works best. |
Keywords
» Artificial intelligence » Deep learning » Machine learning
