Title
Demystifying differentiable programming: shift/reset the penultimate backpropagator
Abstract
Deep learning has seen tremendous success over the past decade in computer vision, machine translation, and gameplay. This success rests crucially on gradient-descent optimization and the ability to “learn” parameters of a neural network by backpropagating observed errors. However, neural network architectures are growing increasingly sophisticated and diverse, which motivates an emerging quest for even more general forms of differentiable programming, where arbitrary parameterized computations can be trained by gradient descent. In this paper, we take a fresh look at automatic differentiation (AD) techniques, and especially aim to demystify the reverse-mode form of AD that generalizes backpropagation in neural networks. We uncover a tight connection between reverse-mode AD and delimited continuations, which permits implementing reverse-mode AD purely via operator overloading and without managing any auxiliary data structures. We further show how this formulation of AD can be fruitfully combined with multi-stage programming (staging), leading to an efficient implementation that combines the performance benefits of deep learning frameworks based on explicit reified computation graphs (e.g., TensorFlow) with the expressiveness of pure library approaches (e.g., PyTorch).
Year
DOI
Venue
2019
10.1145/3341700
PACMPL
Field
DocType
Volume
Data structure,Gradient descent,Operator overloading,Delimited continuation,Computer science,Automatic differentiation,Theoretical computer science,Artificial intelligence,Deep learning,Backpropagation,Artificial neural network
Journal
3
Issue
Citations 
PageRank 
ICFP
0
0.34
References 
Authors
0
6
Name
Order
Citations
PageRank
Fei Wang120340.33
Daniel Zheng200.34
James Decker300.34
Wu, Xilun411.03
Grégory Essertel5104.24
Tiark Rompf674345.86