MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning
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arXiv
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| Autori principali: | , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866910791789182976 |
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| author | Chergui, Abdelmadjid Bezirganyan, Grigor Sellami, Sana Berti-Équille, Laure Fournier, Sébastien |
| author_facet | Chergui, Abdelmadjid Bezirganyan, Grigor Sellami, Sana Berti-Équille, Laure Fournier, Sébastien |
| contents | Choosing a suitable deep learning architecture for multimodal data fusion is a challenging task, as it requires the effective integration and processing of diverse data types, each with distinct structures and characteristics. In this paper, we introduce MixMAS, a novel framework for sampling-based mixer architecture search tailored to multimodal learning. Our approach automatically selects the optimal MLP-based architecture for a given multimodal machine learning (MML) task. Specifically, MixMAS utilizes a sampling-based micro-benchmarking strategy to explore various combinations of modality-specific encoders, fusion functions, and fusion networks, systematically identifying the architecture that best meets the task's performance metrics. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18437 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning Chergui, Abdelmadjid Bezirganyan, Grigor Sellami, Sana Berti-Équille, Laure Fournier, Sébastien Machine Learning Choosing a suitable deep learning architecture for multimodal data fusion is a challenging task, as it requires the effective integration and processing of diverse data types, each with distinct structures and characteristics. In this paper, we introduce MixMAS, a novel framework for sampling-based mixer architecture search tailored to multimodal learning. Our approach automatically selects the optimal MLP-based architecture for a given multimodal machine learning (MML) task. Specifically, MixMAS utilizes a sampling-based micro-benchmarking strategy to explore various combinations of modality-specific encoders, fusion functions, and fusion networks, systematically identifying the architecture that best meets the task's performance metrics. |
| title | MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2412.18437 |