Visual Cortex and Deep Networks: Learning Invariant Representations

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Neural-Symbolic Computing: An Effective Methodology for Principled Integration of Machine Learning and Reasoning (2019, background): “Studies in neuroscience show that some areas in the brain are used for processing input signals e.g. visual cortices for images [20, 37], while other areas are responsible for logical thinking and reasoning [43].”
Unmasking Clever Hans predictors and assessing what machines really learn (2019, background): “[43, 68]), learning them takes significantly longer, requires orders of magnitude more examples and the learned concepts are less transferable to other tasks.”
Why and when can deep-but not shallow-networks avoid the curse of dimensionality: A review (2016, background): “One may then ask about the relation of these results with i-theory([47]).”