From bd3b4e7d8a436685f8b676da8f6ffe9498ab2e3f Mon Sep 17 00:00:00 2001 From: Yuchen Pei Date: Thu, 1 Jul 2021 15:16:19 +1000 Subject: Added copyright notices and license headers to website content. also removed more unused files. --- microposts/rnn-fsm.md | 14 -------------- 1 file changed, 14 deletions(-) delete mode 100644 microposts/rnn-fsm.md (limited to 'microposts/rnn-fsm.md') diff --git a/microposts/rnn-fsm.md b/microposts/rnn-fsm.md deleted file mode 100644 index 61b500f..0000000 --- a/microposts/rnn-fsm.md +++ /dev/null @@ -1,14 +0,0 @@ ---- -date: 2018-05-11 ---- -### Some notes on RNN, FSM / FA, TM and UTM - -Related to [a previous micropost](#neural-turing-machine). - -[These slides from Toronto](http://www.cs.toronto.edu/~rgrosse/csc321/lec9.pdf) are a nice introduction to RNN (recurrent neural network) from a computational point of view. It states that RNN can simulate any FSM (finite state machine, a.k.a. finite automata abbr. FA) with a toy example computing the parity of a binary string. - -[Goodfellow et. al.'s book](http://www.deeplearningbook.org/contents/rnn.html) (see page 372 and 374) goes one step further, stating that RNN with a hidden-to-hidden layer can simulate Turing machines, and not only that, but also the *universal* Turing machine abbr. UTM (the book referenced [Siegelmann-Sontag](https://www.sciencedirect.com/science/article/pii/S0022000085710136)), a property not shared by the weaker network where the hidden-to-hidden layer is replaced by an output-to-hidden layer (page 376). - -By the way, the RNN with a hidden-to-hidden layer has the same architecture as the so-called linear dynamical system mentioned in [Hinton's video](https://www.coursera.org/learn/neural-networks/lecture/Fpa7y/modeling-sequences-a-brief-overview). - -From what I have learned, the universality of RNN and feedforward networks are therefore due to different arguments, the former coming from Turing machines and the latter from an analytical view of approximation by step functions. -- cgit v1.2.3