Memory Fusion Network for Multi-view Sequential Learning

Understand

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.