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Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits

Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits

Paul W. Wilson
Fabio Zanasi
ArXiv (abs)PDFHTML

Papers citing "Reverse Derivative Ascent: A Categorical Approach to Learning Boolean Circuits"

6 / 6 papers shown
Title
Learning Functors using Gradient Descent
Learning Functors using Gradient Descent
Bruno Gavranovic
29
5
0
15 Sep 2020
LUTNet: Rethinking Inference in FPGA Soft Logic
LUTNet: Rethinking Inference in FPGA Soft Logic
Erwei Wang
James J. Davis
P. Cheung
George A. Constantinides
52
66
0
01 Apr 2019
Differentiable Causal Computations via Delayed Trace
Differentiable Causal Computations via Delayed Trace
David Sprunger
Shin-ya Katsumata
36
22
0
04 Mar 2019
Backprop as Functor: A compositional perspective on supervised learning
Backprop as Functor: A compositional perspective on supervised learning
Brendan Fong
David I. Spivak
Rémy Tuyéras
76
95
0
28 Nov 2017
An overview of gradient descent optimization algorithms
An overview of gradient descent optimization algorithms
Sebastian Ruder
ODL
213
6,206
0
15 Sep 2016
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
Matthieu Courbariaux
Yoshua Bengio
J. David
MQ
221
2,994
0
02 Nov 2015

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