<p>I find that works on computational power and complexity of neural nets are all from 1980s/1990s or even earlier. <a href="http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.7434&amp;rep=rep1&amp;type=pdf">Surveys</a> and <a href="http://mitpress.mit.edu/books/circuit-complexity-and-neural-networks">books</a> are also from that time. Personally, I find problems in this field are of importance no matter in theory or application. </p>

<p>Why is there few people care about it now? Is it because open problems are almost solved or they are too hard to keep researchers moving on? Are there researches on this topic now?</p>

CC BY-SA 3.0
2014-04-27T13:36:33.157Z
I don't think you're looking very hard. This is a hot topic right now, but you should look for "deep learning" instead of "neural nets"
Suresh Venkat
CC BY-SA 3.0
2014-04-27T13:58:16.737Z
@ Suresh Venkat I start concerning on this topic from [deep learning](http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf), while I don't think people working on deep learning care about complexity (except Bengio). They care more about algorithm and model. Could you send me any materials on what you mean? Thank you very much.
zenos
CC BY-SA 3.0
2014-04-27T15:28:46.367Z
the field is often more pragmatic than theoretical and in ML there is a general attitude of "just throw more hardware" at hard problems & not worry too much about intrinsic complexity. in many ways they measure complexity different. it is highly problem dependent how well networks converge. a bigger/more relevant question in this area is how much improved recognition performance can be achieved with greater cpu/time. yes there are many huge open problems in the area.
vzn
<circuit-complexity>
Is computational complexity of neural networks so old-fashioned?
zenos
# Is computational complexity of neural networks so old-fashioned?

(Asked by: username_0 on 2014-04-27)

I find that works on computational power and complexity of neural nets are all from 1980s/1990s or even earlier. [Surveys](http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.70.7434&rep=rep1&type=pdf) and [books](http://mitpress.mit.edu/books/circuit-complexity-and-neural-networks) are also from that time. Personally, I find problems in this field are of importance no matter in theory or application.

Why is there few people care about it now? Is it because open problems are almost solved or they are too hard to keep researchers moving on? Are there researches on this topic now?

**Tags:** circuit-complexity
> **Comments:**
> **username_1**: I don't think you're looking very hard. This is a hot topic right now, but you should look for "deep learning" instead of "neural nets"  
> **username_0**: @ Suresh Venkat I start concerning on this topic from [deep learning](http://www.iro.umontreal.ca/~bengioy/papers/ftml.pdf), while I don't think people working on deep learning care about complexity (except Bengio). They care more about algorithm and model. Could you send me any materials on what you mean? Thank you very much.  
> **username_2**: the field is often more pragmatic than theoretical and in ML there is a general attitude of "just throw more hardware" at hard problems & not worry too much about intrinsic complexity. in many ways they measure complexity different. it is highly problem dependent how well networks converge. a bigger/more relevant question in this area is how much improved recognition performance can be achieved with greater cpu/time. yes there are many huge open problems in the area.  

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## 0 answers

