Cited by 855Multimodal Machine Learning ApplicationsTime Series Analysis and ForecastingTopic Modeling

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Memory Fusion Network for Multi-view Sequential Learning

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Zadeh, Amir, Liang, Paul Pu, Mazumder, Navonil, Poria, Soujanya, Cambria, Erik, Morency, Louis-Philippe·2018·Proceedings of the AAAI Conference on Artificial Intelligence

DOI 10.1609/aaai.v32i1.12021

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Abstract

Multi-view sequential learning is a fundamental problem in machine learning dealing with multi-view sequences. In a multi-view sequence, there exists two forms of interactions between different views: view-specific interactions and cross-view interactions. In this paper, we present a new neural architecture for multi-view sequential learning called the Memory Fusion Network (MFN) that explicitly accounts for both interactions in a neural architecture and continuously models them through time. The first component of the MFN is called the System of LSTMs, where view-specific interactions are learned in isolation through assigning an LSTM function to each view. The cross-view interactions are then identified using a special attention mechanism called the Delta-memory Attention Network (DMAN) and summarized through time with a Multi-view Gated Memory. Through extensive experimentation, MFN is compared to various proposed approaches for multi-view sequential learning on multiple publicly available benchmark datasets. MFN outperforms all the multi-view approaches. Furthermore, MFN outperforms all current state-of-the-art models, setting new state-of-the-art results for all three multi-view datasets.

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Authors: Zadeh, A., Liang, P. P., Mazumder, N., Poria, S., Cambria, E., & Morency, L.-P.
Affiliations: Carnegie Mellon University (US)
Instituto Politécnico Nacional (MX)
Nanyang Technological University (SG)
ORCID: Zadeh, Amir
Liang, Paul Pu
Poria, Soujanya
Cambria, Erik
Morency, Louis-Philippe
Venue: Proceedings of the AAAI Conference on Artificial Intelligence
Publisher: Association for the Advancement of Artificial Intelligence (AAAI)
Published: 2018-04-27
Volume: 32
Issue: 1
ISSN: 2159-5399 (ISSN-L)
2374-3468
DOI: 10.1609/aaai.v32i1.12021
OpenAlex: W2787581402
Cited by: 855
References: 36
Citations by year: 2026: 100 · 2025: 169 · 2024: 163 · 2023: 125 · 2022: 118 · 2021: 87 · 2020: 48 · 2019: 29 · 2018: 16
FWCI: 13.57
Type: conference paper
Language: en
Open access: diamond
doi.org
Locations: Proceedings of the AAAI Conference on Artificial Intelligence (publishedVersion)
AAAI Publications (The Association for the Advancement of Artificial Intelligence (AAAI)) (submittedVersion)
Topics: Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Physical Sciences › Computer Science › Signal Processing › Time Series Analysis and Forecasting
Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
Keywords: LSTM, attention mechanism, temporal modeling

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