You need to log in to edit.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

You can create a new account if you don't have one.

Or, discuss a change on Slack.

1 code implementation • 20 Dec 2017 • Michael Bukatin, Jon Anthony

1) Dataflow matrix machines (DMMs) generalize neural nets by replacing streams of numbers with linear streams (streams supporting linear combinations), allowing arbitrary input and output arities for activation functions, countable-sized networks with finite dynamically changeable active part capable of unbounded growth, and a very expressive self-referential mechanism.

1 code implementation • 3 May 2017 • Michael Bukatin, Jon Anthony

We overview dataflow matrix machines as a Turing complete generalization of recurrent neural networks and as a programming platform.

1 code implementation • 4 Oct 2016 • Michael Bukatin, Steve Matthews, Andrey Radul

Dataflow matrix machines are self-referential generalized recurrent neural nets.

Programming Languages

1 code implementation • 30 Jun 2016 • Michael Bukatin, Steve Matthews, Andrey Radul

Dataflow matrix machines arise naturally in the context of synchronous dataflow programming with linear streams.

1 code implementation • 17 May 2016 • Michael Bukatin, Steve Matthews, Andrey Radul

Dataflow matrix machines are a powerful generalization of recurrent neural networks.

1 code implementation • 29 Mar 2016 • Michael Bukatin, Steve Matthews, Andrey Radul

Dataflow matrix machines are a powerful generalization of recurrent neural networks.

no code implementations • 15 Dec 2015 • Michael Bukatin, Steve Matthews

We consider two classes of computations which admit taking linear combinations of execution runs: probabilistic sampling and generalized animation.

Cannot find the paper you are looking for? You can
Submit a new open access paper.

Contact us on:
hello@paperswithcode.com
.
Papers With Code is a free resource with all data licensed under CC-BY-SA.