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DOI: 10.3389/fcomp.2021.624683

One-Liner

late fusion of multimodal signal on the CTP task using transformers, mobilnet, yamnet, and mockingjay

Novelty

• Similar to Martinc 2021 and Shah 2021 but actually used the the current Neural-Network state of the art
• Used late fusion again after the base model training
• Proposed that inconsistency in the diagnoses of MMSE scores could be a great contributing factor to multi-task learning performance hindrance

Notable Methods

• Proposed base model for transfer learning from text based on MobileNet (image), YAMNet (audio), Mockingjay (speech) and BERT (text)
• Data all sourced from recording/transcribing/recognizing CTP task

Key Figs

Figure 3 and 4

This figure tells us the late fusion architecture used

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regulating zinc uptake

see Zipf’s Law