From f544f1ec2d3beee6b2ac39e5530b358ee906eefc Mon Sep 17 00:00:00 2001
From: Shan Carter <shanjcarter@gmail.com>
Date: Mon, 13 Mar 2017 13:08:06 -0700
Subject: [PATCH] checkpoint

---
 examples/article.html | 17 +++--------------
 1 file changed, 3 insertions(+), 14 deletions(-)

diff --git a/examples/article.html b/examples/article.html
index 332e4ea..8fa3f5d 100644
--- a/examples/article.html
+++ b/examples/article.html
@@ -20,7 +20,7 @@
 <d-abstract>
   <p>We’ve seen a growing number of attempts to augment RNNs with new properties. Four directions stand out as particularly exciting: neural turing machines, attentional interfaces, adaptive computation time and neural programmers.</p>
   <p>Individually, these techniques are all potent extensions of RNNs, but the really striking thing is that they can be combined together, and seem to just be points in a broader space. Further, they all rely on the same underlying trick — something called attention — to work.</p>
-  </d-abstract>
+</d-abstract>
 
 <d-article>
   <h2>Neural Turing Machines</h2>
@@ -54,7 +54,6 @@
       publisher={Cambridge Univ Press},
       doi={10.1017/S0140525X10000968}
     }
-
     @article{dong2014image,
       title={Image super-resolution using deep convolutional networks},
       author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
@@ -62,7 +61,6 @@
       year={2014},
       url={https://arxiv.org/pdf/1501.00092.pdf}
     }
-
     @article{dumoulin2016adversarially,
       title={Adversarially Learned Inference},
       author={Dumoulin, Vincent and Belghazi, Ishmael and Poole, Ben and Lamb, Alex and Arjovsky, Martin and Mastropietro, Olivier and Courville, Aaron},
@@ -70,7 +68,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1606.00704.pdf}
     }
-
     @article{dumoulin2016guide,
       title={A guide to convolution arithmetic for deep learning},
       author={Dumoulin, Vincent and Visin, Francesco},
@@ -78,7 +75,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1603.07285.pdf}
     }
-
     @article{donahue2016adversarial,
       title={Adversarial Feature Learning},
       author={Donahue, Jeff and Kr{\"a}henb{\"u}hl, Philipp and Darrell, Trevor},
@@ -86,7 +82,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1605.09782.pdf}
     }
-
     @article{gauthier2014conditional,
       title={Conditional generative adversarial nets for convolutional face generation},
       author={Gauthier, Jon},
@@ -95,7 +90,6 @@
       year={2014},
       url={http://www.foldl.me/uploads/papers/tr-cgans.pdf}
     }
-
     @article{henaff2015geodesics,
       title={Geodesics of learned representations},
       author={H{\'e}naff, Olivier J and Simoncelli, Eero P},
@@ -103,7 +97,6 @@
       year={2015},
       url={https://arxiv.org/pdf/1511.06394.pdf}
     }
-
     @article{johnson2016perceptual,
       title={Perceptual losses for real-time style transfer and super-resolution},
       author={Johnson, Justin and Alahi, Alexandre and Fei-Fei, Li},
@@ -111,7 +104,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1603.08155.pdf}
     }
-
     @article{mordvintsev2015inceptionism,
       title={Inceptionism: Going deeper into neural networks},
       author={Mordvintsev, Alexander and Olah, Christopher and Tyka, Mike},
@@ -119,14 +111,12 @@
       year={2015},
       url={https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html}
     }
-
     @misc{mordvintsev2016deepdreaming,
       title={DeepDreaming with TensorFlow},
       author={Mordvintsev, Alexander},
       year={2016},
       url={https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb},
     }
-
     @article{radford2015unsupervised,
       title={Unsupervised representation learning with deep convolutional generative adversarial networks},
       author={Radford, Alec and Metz, Luke and Chintala, Soumith},
@@ -134,7 +124,6 @@
       year={2015},
       url={https://arxiv.org/pdf/1511.06434.pdf}
     }
-
     @inproceedings{salimans2016improved,
       title={Improved techniques for training gans},
       author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
@@ -143,7 +132,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1606.03498.pdf}
     }
-
     @article{shi2016deconvolution,
       title={Is the deconvolution layer the same as a convolutional layer?},
       author={Shi, Wenzhe and Caballero, Jose and Theis, Lucas and Huszar, Ferenc and Aitken, Andrew and Ledig, Christian and Wang, Zehan},
@@ -151,7 +139,6 @@
       year={2016},
       url={https://arxiv.org/pdf/1609.07009.pdf}
     }
-
     @inproceedings{shi2016real,
       title={Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network},
       author={Shi, Wenzhe and Caballero, Jose and Husz{\'a}r, Ferenc and Totz, Johannes and Aitken, Andrew P and Bishop, Rob and Rueckert, Daniel and Wang, Zehan},
@@ -163,3 +150,5 @@
     }
   </script>
 </d-bibliography>
+
+<distill-appendix></distill-appendix>