Visual Backpropagation

06/06/2019
by   Roy S. Freedman, et al.
0

We show how a declarative functional programming specification of backpropagation yields a visual and transparent implementation within spreadsheets. We call our method Visual Backpropagation. This backpropagation implementation exploits array worksheet formulas, manual calculation, and has a sequential order of computation similar to the processing of a systolic array. The implementation uses no hidden macros nor user-defined functions; there are no loops, assignment statements, or links to any procedural programs written in conventional languages. As an illustration, we compare a Visual Backpropagation solution to a Tensorflow (Python) solution on a standard regression problem.

READ FULL TEXT
research
06/19/2018

ASIC Implementation of Time-Domain Digital Backpropagation with Deep-Learned Chromatic Dispersion Filters

We consider time-domain digital backpropagation with chromatic dispersio...
research
09/27/2019

Backpropagation in the Simply Typed Lambda-calculus with Linear Negation

Backpropagation is a classic automatic differentiation algorithm computi...
research
02/16/2021

Coupled-Channel Enhanced SSFM for Digital Backpropagation in WDM Systems

A novel technique for digital backpropagation (DBP) in wavelength-divisi...
research
09/11/2020

Activation Relaxation: A Local Dynamical Approximation to Backpropagation in the Brain

The backpropagation of error algorithm (backprop) has been instrumental ...
research
04/05/2023

Predictive Coding as a Neuromorphic Alternative to Backpropagation: A Critical Evaluation

Backpropagation has rapidly become the workhorse credit assignment algor...
research
03/22/2021

Tangent Space Backpropagation for 3D Transformation Groups

We address the problem of performing backpropagation for computation gra...

Please sign up or login with your details

Forgot password? Click here to reset