MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning

I tiakina i:

Ngā taipitopito rārangi puna kōrero
I whakaputaina i:	arXiv.org (Dec 24, 2024), p. n/a
Kaituhi matua:	Chergui, Abdelmadjid
Ētahi atu kaituhi:	Bezirganyan, Grigor, Sellami, Sana, Berti-Équille, Laure, Fournier, Sébastien
I whakaputaina:	Cornell University Library, arXiv.org
Ngā marau:	Performance measurement Data integration Deep learning Machine learning Data search Sampling
Urunga tuihono:	Citation/Abstract Full text outside of ProQuest
Ngā Tūtohu:	Tāpirihia he Tūtohu Kāore He Tūtohu, Me noho koe te mea tuatahi ki te tūtohu i tēnei pūkete!

Whakaahuatanga
Whakarāpopotonga:	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.
ISSN:	2331-8422
Puna:	Engineering Database