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cs.LG2019
Scaling up deep neural networks: a capacity allocation perspective
Jonathan Donier
Following the recent work on capacity allocation, we formulate the conjecture that the shattering problem in deep neural networks can only be avoided if the capacity propagation th…
cs.LG2019★ 2 cited
Capacity allocation through neural network layers
Jonathan Donier
Capacity analysis has been recently introduced as a way to analyze how linear models distribute their modelling capacity across the input space. In this paper, we extend the notion…
cs.LG2019★ 3 cited
Capacity allocation analysis of neural networks: A tool for principled architecture design
Jonathan Donier
Designing neural network architectures is a task that lies somewhere between science and art. For a given task, some architectures are eventually preferred over others, based on a…