Interpretable tensor fusion

• Main Authors: VARSHNEYA, Saurabh, LEDENT, Antoine, LIZNERSKI, Philipp, BALINSKYY, Andriy, MEHTA, Purvanshi, MUSTAFA, Waleed, KLOFT, Marius
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2024
• Subjects: Tensor Fusion; Multiple Kernel Learning; Interpretability; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/9305; https://ink.library.smu.edu.sg/context/sis_research/article/10305/viewcontent/0557__1_.pdf
• Institution: Singapore Management University

Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type. However, practical applications may encompass data of diverse types, such as text, images, and audio. We introduce interpretable tensor fusion (InTense), a multimodal learning method tra...