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This week in Artificial Intelligence & Security - #38 of 2016
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<abbr class="published" title="2016-09-25T00:00:00">
Sun 25 September 2016 </abbr>
<span class="label"> Category</span>
<a href="./category/ai-ml-security.html"><i class="icon-folder-open"></i>AI & ML, Security</a>
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<p><img alt="cyber" height="300px" src="./cyberpunk/11.gif" width="400px" /></p>
<h1>In the Arxivs</h1>
<ul>
<li><a href="https://arxiv.org/abs/1609.05521">Playing FPS Games with Deep Reinforcement Learning</a>.</li>
<li><a href="http://arxiv.org/abs/1609.03126">Energy-based Generative Adversarial Network</a>.</li>
<li><a href="http://arxiv.org/abs/1609.04836">On Large-Batch Training for Deep Learning: Generalization Gap and Sharp Minima</a>.</li>
<li><a href="https://arxiv.org/abs/1609.05473">SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient</a>.</li>
<li><a href="https://arxiv.org/abs/1609.06647">Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge</a>.</li>
<li><a href="http://arxiv.org/abs/1609.05143">Target-driven Visual Navigation in Indoor Scenes using Deep Reinforcement Learning</a>.</li>
<li><a href="http://arxiv.org/abs/1609.07152">Input Convex Neural Networks</a>.</li>
<li><a href="http://arxiv.org/abs/1609.06693">SoftTarget Regularization: An Effective Technique to Reduce Over-Fitting in Neural Networks</a> (and also <a href="https://arxiv.org/abs/1605.00055">DisturbLabel: Regularizing CNN on the Loss Layer</a>).</li>
</ul>
<h1>In the News</h1>
<ul>
<li><a href="http://www.unofficialgoogledatascience.com/2016/08/next-generation-tools-for-data-science.html">Next generation tools for data science</a>.</li>
<li><a href="http://www.asimovinstitute.org/neural-network-zoo/">The Neural Network Zoo</a>.</li>
<li><a href="https://blog.cardiogr.am/three-challenges-for-artificial-intelligence-in-medicine-dfb9993ae750">Three Challenges for Artificial Intelligence in Medicine</a>.</li>
</ul>
<h1>Interesting Videos</h1>
<ul>
<li><a href="https://www.youtube.com/watch?feature=youtu.be&utm_campaign=Revue%20newsletter&utm_medium=Newsletter&utm_source=revue&v=wofXCQXq1pg">Prof. Yann LeCun - Deep Learning and the Future of AI</a>. "Unsupervised learning is the Dark Matter (Energy) of ML".</li>
<li><a href="https://www.youtube.com/watch?v=eyovmAtoUx0&feature=youtu.be">Bay Area Deep Learning School Day 1 at CEMEX auditorium, Stanford</a>.</li>
<li><a href="https://www.youtube.com/watch?v=9dXiAecyJrY">Bay Area Deep Learning School Day 2 at CEMEX auditorium, Stanford</a>.</li>
<li><a href="https://www.youtube.com/watch?v=QPkb5VcgXAM">Soumith Chintala's 'A path to unsupervised learning through adversarial networks'</a>.</li>
<li><a href="https://www.youtube.com/watch?v=NKiwFF_zBu4">Ian Goodfellow - Practical Methodology for Deploying Machine Learning</a>. </li>
</ul>
<h1>Some Slides & Classes</h1>
<ul>
<li><a href="https://docs.google.com/presentation/d/1CRSAs2WOKo5mFhh5Iu-xkDfyJsg_NDL1r5dRtj6_aHo/edit#slide=id.g1714a2be67_1_253">Deep learning for audio-based music recommendation
</a>.</li>
<li><a href="https://people.orie.cornell.edu/andrew/orie6741/">Bayesian Machine Learning</a>.</li>
<li><a href="https://www.youtube.com/user/gametheoryonline/playlists">Game Theory Online</a>.</li>
</ul>
<h1>Github</h1>
<ul>
<li><a href="https://github.com/openai/cleverhans">OpenAI's cleverhans: A library for benchmarking vulnerability to adversarial examples</a>.</li>
<li><a href="https://github.com/LantaoYu/SeqGAN">Implementation of Sequence Generative Adversarial Nets with Policy Gradient</a>.</li>
<li><a href="https://github.com/iassael/learning-to-communicate">Learning to Communicate with Deep Multi-Agent Reinforcement Learning</a>.</li>
<li><a href="https://github.com/alxndrkalinin/awesome-deepneuroimage">A curated list of awesome deep learning applications in the field of neurological image analysis</a>.</li>
<li><a href="">Learning to Communicate with Deep Multi-Agent Reinforcement Learning</a>.</li>
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