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Sequential Coordination of Deep Models for Learning Visual Arithmetic
[article]
2018
arXiv
pre-print
Achieving machine intelligence requires a smooth integration of perception and reasoning, yet models developed to date tend to specialize in one or the other; sophisticated manipulation of symbols acquired from rich perceptual spaces has so far proved elusive. Consider a visual arithmetic task, where the goal is to carry out simple arithmetical algorithms on digits presented under natural conditions (e.g. hand-written, placed randomly). We propose a two-tiered architecture for tackling this
arXiv:1809.04988v1
fatcat:2iyvqtoh65asjefqazbcfo7mni