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

Shranjeno v:
izdano v:arXiv.org (Dec 24, 2024), p. n/a
Glavni avtor: Chergui, Abdelmadjid
Drugi avtorji: Bezirganyan, Grigor, Sellami, Sana, Berti-Équille, Laure, Fournier, Sébastien
Izdano:
Cornell University Library, arXiv.org
Teme:
Performance measurement
Data integration
Deep learning
Machine learning
Data search
Sampling
Online dostop:Citation/Abstract
Full text outside of ProQuest
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045 0 |b d20241224 
100 1 |a Chergui, Abdelmadjid 
245 1 |a MixMAS: A Framework for Sampling-Based Mixer Architecture Search for Multimodal Fusion and Learning 
260 |b Cornell University Library, arXiv.org  |c Dec 24, 2024 
513 |a Working Paper 
520 3 |a 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. 
653 |a Performance measurement 
653 |a Data integration 
653 |a Deep learning 
653 |a Machine learning 
653 |a Data search 
653 |a Sampling 
700 1 |a Bezirganyan, Grigor 
700 1 |a Sellami, Sana 
700 1 |a Berti-Équille, Laure 
700 1 |a Fournier, Sébastien 
773 0 |t arXiv.org  |g (Dec 24, 2024), p. n/a 
786 0 |d ProQuest  |t Engineering Database 
856 4 1 |3 Citation/Abstract  |u https://www.proquest.com/docview/3149106835/abstract/embedded/ZKJTFFSVAI7CB62C?source=fedsrch 
856 4 0 |3 Full text outside of ProQuest  |u http://arxiv.org/abs/2412.18437