Fetching the paper…
Reading the bibliography…
Consider a feedforward neural network $\psi: \mathbb{R}^d\rightarrow \mathbb{R}^d$ such that $\psi\approx \nabla f$, where $f:\mathbb{R}^d \rightarrow \mathbb{R}$ is a smooth function, therefore $\psi$ must satisfy $\partial_j \psi_i = \partial_i \psi_j$ pointwise.
Learning representations by back-propagating errors
David E Rumelhart, Geoffrey E Hinton, and Ronald J Williams · 1986
Earlier work this paper cites.
Products of experts
Geoffrey E Hinton · 1999
Earlier work this paper cites.
A mathematical view of automatic differentiation
Andreas Griewank · 2003
Earlier work this paper cites.
Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
Earlier work this paper cites.
Learning deep architectures for AI
Yoshua Bengio · 2009
Earlier work this paper cites.
An epsilon of room, I: real analysis
Terence Tao · 2010
Cited alongside, same era.
A connection between score matching and denoising autoencoders
Pascal Vincent · 2011
Cited alongside, same era.
Who invented the reverse mode of differentiation?
Andreas Griewank · 2012
Cited alongside, same era.
What regularized auto-encoders learn from the data-generating distribution
Guillaume Alain and Yoshua Bengio · 2014
Cited alongside, same era.
Deep learning in neural networks: An overview
Jürgen Schmidhuber · 2015
Cited alongside, same era.
The expressive power of neural networks: A view from the width
Zhou Lu, Hongming Pu, Feicheng Wang, Zhiqiang Hu, and Liwei Wang · 2017
Later among the works it cites.
Automatic differentiation in machine learning: a survey
Atilim Gunes Baydin, Barak A Pearlmutter, Alexey Andreyevich Radul, and Jeffrey Mark Siskind · 2018
Later among the works it cites.
Deep energy estimator networks
Saeed Saremi, Arash Mehrjou, Bernhard Schölkopf, and Aapo Hyvärinen · 2018
Later among the works it cites.
Annealed denoising score matching: Learning energy-based models in high-dimensional spaces
Zengyi Li, Yubei Chen, and Friedrich T Sommer · 2019
Closest in time.
Saeed Saremi and Aapo Hyvärinen · 2019
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…