We are very excited to join forces with MLCommons and OctoML.ai! Contact Grigori Fursin for more details!

Differentiable Causal Computations via Delayed Trace

lib:434190b0804bd8ac (v1.0.0)

Authors: David Sprunger,Shin-ya Katsumata
ArXiv: 1903.01093
Document:  PDF  DOI 
Abstract URL: http://arxiv.org/abs/1903.01093v1


We investigate causal computations taking sequences of inputs to sequences of outputs where the $n$th output depends on the first $n$ inputs only. We model these in category theory via a construction taking a Cartesian category $C$ to another category $St(C)$ with a novel trace-like operation called "delayed trace", which misses yanking and dinaturality axioms of the usual trace. The delayed trace operation provides a feedback mechanism in $St(C)$ with an implicit guardedness guarantee. When $C$ is equipped with a Cartesian differential operator, we construct a differential operator for $St(C)$ using an abstract version of backpropagation through time, a technique from machine learning based on unrolling of functions. This obtains a swath of properties for backpropagation through time, including a chain rule and Schwartz theorem. Our differential operator is also able to compute the derivative of a stateful network without requiring the network to be unrolled.

Relevant initiatives  

Related knowledge about this paper Reproduced results (crowd-benchmarking and competitions) Artifact and reproducibility checklists Common formats for research projects and shared artifacts Reproducibility initiatives

Comments  

Please log in to add your comments!
If you notice any inapropriate content that should not be here, please report us as soon as possible and we will try to remove it within 48 hours!