mirror of
https://github.com/wassname/template.git
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Ludwig Schubert
221 lines
8.6 KiB
HTML
221 lines
8.6 KiB
HTML
<!doctype html>
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<head>
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<meta charset="utf8">
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<script src="../dist/template.v2.js"></script>
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<d-front-matter>
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<script type="text/yml">
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title: Demo Title Attention and Augmented Recurrent Neural Networks
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published: Jan 10, 2017
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authors:
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- Chris Olah:
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- Shan Carter: http://shancarter.com
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affiliations:
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- Google Brain
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- Google Brain: http://g.co/brain
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</script>
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</d-front-matter>
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</head>
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<body>
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<d-article>
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<d-title>
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<h1>Attention and Augmented Recurrent Neural Networks</h1>
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<h2>Some people want a deck</h2>
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</d-title>
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<d-byline></d-byline>
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<d-abstract>
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<p>This is the first paragraph of the article. Test a long — dash -- here it is.</p>
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</d-abstract>
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<p>This is the first paragraph of the article. Test a long — dash -- here it is.</p>
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<p>Test for owner's possessive. Test for "quoting a passage." And another sentence. Or two. Some flopping fins; for diving.</p>
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<hr>
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<div style="max-width: 800px; background-color: red; height: 100px; border-radius: 50px;"></div>
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<p>Here's a test of an inline equation <d-math>c = a^2 + b^2</d-math>. And then there's a block equation:</p>
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<d-math block>
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c = \pm \sqrt{ \sum_{i=0}^{n}{a^{222} + b^2}}
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</d-math>
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<p>
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Math can also be quite involved:
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<d-math block>
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f(x) = \int_{-\infty}^\infty\hat f(\xi)\,e^{2 \pi i \xi x}\,d\xi
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</d-math>
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<d-math block>
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\frac{1}{\Bigl(\sqrt{\phi \sqrt{5}}-\phi\Bigr) e^{\frac25 \pi}} = 1+\frac{e^{-2\pi}} {1+\frac{e^{-4\pi}} {1+\frac{e^{-6\pi}} {1+\frac{e^{-8\pi}} {1+\cdots} } } }
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</d-math>
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</p>
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<p>We can<d-cite key="mercier2011humans"></d-cite> also cite <d-cite key="gregor2015draw,mercier2011humans"></d-cite> external publications. <d-cite key="dong2014image,dumoulin2016guide,mordvintsev2015inceptionism"></d-cite></p>
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<p>We should also be testing footnotes<d-footnote>This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote. This will become a hoverable footnote.</d-footnote>. There are multiple footnotes, and they appear in the appendix<d-footnote>Given I have coded them right. Also, here's math in a footnote: <d-math>c = \sum_0^i{x}</d-math>. Also, a citation. Box-ception<d-cite key='gregor2015draw'></d-cite>!</d-footnote> as well.</p>
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<table>
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<thead>
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<tr><th>First</th><th>Second</th><th>Third</th></tr>
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</thead>
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<tbody>
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<tr><td>23</td><td>654</td><td>23</td></tr>
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<tr><td>14</td><td>54</td><td>34</td></tr>
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<tr><td>234</td><td>54</td><td>23</td></tr>
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</tbody>
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</table>
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<h2>Displaying code snippets</h2>
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<p>Some inline javascript:<d-code language="javascript">var x = 25;</d-code></p>
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<p>Here's a javascript code block.</p>
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<d-code block language="javascript">
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var x = 25;
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function(x){
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return x * x;
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}
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</d-code>
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<p>We also support python.</p>
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<d-code block language="python">
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# Python 3: Fibonacci series up to n
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def fib(n):
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a, b = 0, 1
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while a < n:
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print(a, end=' ')
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a, b = b, a+b
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</d-code>
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<d-figure id="last-figure"></d-figure>
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<script>
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const figure = document.querySelector("d-figure#last-figure");
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const initTag = document.createElement("span");
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initTag.textContent = "initialized!"
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figure.appendChild(initTag);
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figure.addEventListener("ready", function() {
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const initTag = figure.querySelector("span");
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initTag.textContent = "ready"
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console.log('ready')
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});
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figure.addEventListener("onscreen", function() {
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const initTag = figure.querySelector("span");
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initTag.textContent = "onscreen"
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console.log('onscreen')
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});
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figure.addEventListener("offscreen", function() {
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const initTag = figure.querySelector("span");
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initTag.textContent = "offscreen!"
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console.log('offscreen')
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});
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</script>
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<p>That's it for the example article!</p>
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<aside>Some text.</aside>
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</d-article>
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<d-appendix>
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<d-acknowledgements>
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<h3>Contributions</h3>
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<p>Some text describing who did what.</p>
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<h4>Reviewers</h4>
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<p>Some text with links describing who reviewed the article.</p>
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</d-acknowledgements>
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<d-footnote-list></d-footnote-list>
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<d-bibliography><script type="text/bibtex">
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@article{gregor2015draw,
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title={DRAW: A recurrent neural network for image generation},
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author={Gregor, Karol and Danihelka, Ivo and Graves, Alex and Rezende, Danilo Jimenez and Wierstra, Daan},
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journal={arXiv preprint arXiv:1502.04623},
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year={2015},
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url ={https://arxiv.org/pdf/1502.04623.pdf}
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}
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@article{mercier2011humans,
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title={Why do humans reason? Arguments for an argumentative theory},
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author={Mercier, Hugo and Sperber, Dan},
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journal={Behavioral and brain sciences},
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volume={34},
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number={02},
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pages={57--74},
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year={2011},
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publisher={Cambridge Univ Press},
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doi={10.1017/S0140525X10000968}
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}
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@article{dong2014image,
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title={Image super-resolution using deep convolutional networks},
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author={Dong, Chao and Loy, Chen Change and He, Kaiming and Tang, Xiaoou},
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journal={arXiv preprint arXiv:1501.00092},
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year={2014},
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url={https://arxiv.org/pdf/1501.00092.pdf}
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}
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@article{dumoulin2016adversarially,
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title={Adversarially Learned Inference},
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author={Dumoulin, Vincent and Belghazi, Ishmael and Poole, Ben and Lamb, Alex and Arjovsky, Martin and Mastropietro, Olivier and Courville, Aaron},
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journal={arXiv preprint arXiv:1606.00704},
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year={2016},
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url={https://arxiv.org/pdf/1606.00704.pdf}
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}
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@article{dumoulin2016guide,
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title={A guide to convolution arithmetic for deep learning},
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author={Dumoulin, Vincent and Visin, Francesco},
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journal={arXiv preprint arXiv:1603.07285},
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year={2016},
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url={https://arxiv.org/pdf/1603.07285.pdf}
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}
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@article{gauthier2014conditional,
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title={Conditional generative adversarial nets for convolutional face generation},
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author={Gauthier, Jon},
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journal={Class Project for Stanford CS231N: Convolutional Neural Networks for Visual Recognition, Winter semester},
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volume={2014},
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year={2014},
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url={http://www.foldl.me/uploads/papers/tr-cgans.pdf}
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}
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@article{johnson2016perceptual,
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title={Perceptual losses for real-time style transfer and super-resolution},
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author={Johnson, Justin and Alahi, Alexandre and Fei-Fei, Li},
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journal={arXiv preprint arXiv:1603.08155},
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year={2016},
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url={https://arxiv.org/pdf/1603.08155.pdf}
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}
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@article{mordvintsev2015inceptionism,
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title={Inceptionism: Going deeper into neural networks},
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author={Mordvintsev, Alexander and Olah, Christopher and Tyka, Mike},
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journal={Google Research Blog},
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year={2015},
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url={https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html}
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}
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@misc{mordvintsev2016deepdreaming,
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title={DeepDreaming with TensorFlow},
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author={Mordvintsev, Alexander},
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year={2016},
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url={https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/tutorials/deepdream/deepdream.ipynb},
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}
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@article{radford2015unsupervised,
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title={Unsupervised representation learning with deep convolutional generative adversarial networks},
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author={Radford, Alec and Metz, Luke and Chintala, Soumith},
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journal={arXiv preprint arXiv:1511.06434},
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year={2015},
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url={https://arxiv.org/pdf/1511.06434.pdf}
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}
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@inproceedings{salimans2016improved,
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title={Improved techniques for training gans},
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author={Salimans, Tim and Goodfellow, Ian and Zaremba, Wojciech and Cheung, Vicki and Radford, Alec and Chen, Xi},
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booktitle={Advances in Neural Information Processing Systems},
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pages={2226--2234},
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year={2016},
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url={https://arxiv.org/pdf/1606.03498.pdf}
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}
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@article{shi2016deconvolution,
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title={Is the deconvolution layer the same as a convolutional layer?},
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author={Shi, Wenzhe and Caballero, Jose and Theis, Lucas and Huszar, Ferenc and Aitken, Andrew and Ledig, Christian and Wang, Zehan},
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journal={arXiv preprint arXiv:1609.07009},
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year={2016},
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url={https://arxiv.org/pdf/1609.07009.pdf}
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}
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</script></d-bibliography>
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<distill-appendix> </distill-appendix>
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</d-appendix>
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</body>
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