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.
1 code implementation • 29 Mar 2016 • Michael Bukatin, Steve Matthews, Andrey Radul
Dataflow matrix machines are a powerful generalization of recurrent neural networks.
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 • 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 • 4 Oct 2016 • Michael Bukatin, Steve Matthews, Andrey Radul
Dataflow matrix machines are self-referential generalized recurrent neural nets.
Programming Languages
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 • 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.
no code implementations • 27 Feb 2023 • András Kornai, Michael Bukatin, Zsolt Zombori
Currently, the dominant paradigm in AI safety is alignment with human values.