# Unsupervised feature learning followed by supervised readout layer (paper)

**URL:** <https://discourse.numenta.org/t/unsupervised-feature-learning-followed-by-supervised-readout-layer-paper/4108>\
**Category:** Lounge\
**Created:** [June 28, 2018, 5:57am UTC](https://discourse.numenta.org/t/unsupervised-feature-learning-followed-by-supervised-readout-layer-paper/4108 "2018-06-28T05:57:21Z")\
**Posts on this page:** 1\
**Page:** 1

<div class="post-metadata">

**Author:** ![Sean\_O\_Connor](https://avatars.discourse-cdn.com/v4/letter/s/34f0e0/32.png) [@Sean\_O\_Connor](https://discourse.numenta.org/u/Sean_O_Connor)\
**Post date:** [June 28, 2018, 5:57am UTC](https://discourse.numenta.org/t/unsupervised-feature-learning-followed-by-supervised-readout-layer-paper/4108/1 "2018-06-28T05:57:21Z")

</div>

I see Krotov and Hopfield have this paper out:  
[https://arxiv.org/abs/1806.10181](https://arxiv.org/abs/1806.10181)  
Where they combine both biologically plausible unsupervised feature learning and a supervised readout layer.

I also have multi-threaded numerical optimizer in Java that I am trying out:  
[https://github.com/S6Regen/Cisco](https://github.com/S6Regen/Cisco)  
I’d like to try that code out on one of AMD’s new server boards, with 2 CPUs, 64 cores and 128 threads. Which is wishful thinking.

There is also a slew of papers on deep neural network (resnet,recurrent) viewed as ordinary differential equations:  
[https://arxiv.org/abs/1804.04272](https://arxiv.org/abs/1804.04272)  
[https://arxiv.org/abs/1806.07366](https://arxiv.org/abs/1806.07366)  
Which fits in with chaos theory of view of compounding non-linearity that was discussed. Since many fractals are defined as differential equations.  
Anyway I should be able to try out a simplified version as some stage.