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Philips Kokoh Prasetyo
Let's make things better
Let's make things better

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Amazon has started a blog for AI on Amazon Web Services (AWS).

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I'm excited that the Google Brain team ( will have a decent presence at ICLR 2017 (, with 20 papers (including 4 papers chosen for oral presentation), plus an additional 4 papers in the workshop track. Of these, 9 of the papers have co-authors from our Brain Residency program (, and another 8 have co-authors who were interns in our group. The Brain affiliated papers are below:

- Understanding deep learning requires rethinking generalization, by Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, Oriol Vinyals, (Intern co-author), Oral
- Neural Architecture Search with Reinforcement Learning, by Barret Zoph and Quoc Le, (Brain Resident co-author), Oral
- Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data, by Nicolas Papernot, Martín Abadi, Úlfar Erlingsson, Ian Goodfellow, Kunal Talwar,, Oral
- Q-Prop: Sample-Efficient Policy Gradient with An Off-Policy Critic, by Shixiang (Shane) Gu, Timothy Lillicrap, Zoubin Ghahramani, Richard E. Turner, Sergey Levine, (Intern co-author), Oral
- Adversarial Machine Learning at Scale, by Alexey Kurakin, Ian J. Goodfellow, Samy Bengio,
- Density estimation using Real NVP, by Laurent Dinh, Jascha Sohl-Dickstein, Samy Bengio, (Intern co-author)
- Learning to Remember Rare Events, by Lukasz Kaiser, Ofir Nachum, Aurko Roy, Samy Bengio, (Brain Resident co-author)
- Categorical Reparameterization with Gumbel-Softmax, by Eric Jang, Shixiang (Shane) Gu, Ben Poole, (Intern co-author)
- HyperNetworks, by David Ha, Andrew Dai, Quoc V. Le, (Brain Resident co-author)
- Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer, by Noam Shazeer, Azalia Mirhoseini, Krzysztof Maziarz, Andy Davis, Quoc Le, Geoffrey Hinton, Jeff Dean, (Brain Resident co-author)
- Learning a Natural Language Interface with Neural Programmer, by Arvind Neelakantan, Quoc V. Le, Martín Abadi, Andrew McCallum, Dario Amodei, (Intern co-author)
- Deep Information Propagation, by Samuel Schoenholz, Justin Gilmer, Surya Ganguli, Jascha Sohl-Dickstein, (Brain Resident co-author)
- Decomposing Motion and Content for Natural Video Sequence Prediction, by Ruben Villegas, Jimei Yang, Seunghoon Hong, Xunyu Lin, Honglak Lee,
- Capacity and Trainability in Recurrent Neural Networks, by Jasmine Collins, Jascha Sohl-Dickstein, David Sussillo, (Brain Resident co-author)
- Unrolled Generative Adversarial Networks, by Luke Metz, Ben Poole, David Pfau, Jascha Sohl-Dickstein, (Brain Resident co-author)
- A Learned Representation For Artistic Style, by Vincent Dumoulin, Jonathon Shlens, Manjunath Kudlur, (Intern co-author)
- Identity Matters in Deep Learning, by Moritz Hardt, Tengyu Ma,
- Latent Sequence Decompositions, by William Chan, Yu Zhang, Quoc Le, Navdeep Jaitly, (Intern co-author)
- Improving policy gradient by exploring under-appreciated rewards, by Ofir Nachum, Mohammad Norouzi, Dale Schuurmans, (Brain Resident co-author)
- Adversarial Training Methods for Semi-Supervised Text Classification, by Takeru Miyato, Andrew M. Dai, Ian Goodfellow,
- Adversarial examples in the physical world, by Alexey Kurakin, Ian J. Goodfellow, Samy Bengio,, Workshop
- Short and Deep: Sketching and Neural Networks, by Amit Daniely, Nevena Lazic, Yoram Singer, Kunal Talwar,, Workshop
- Unsupervised Perceptual Rewards for Imitation Learning, by Pierre Sermanet, Kelvin Xu, Sergey Levine, (Brain Resident co-author), Workshop
- Tuning Recurrent Neural Neworks with Reinforcement Learning, by Natasha Jaques, Shixiang (Shane) Gu, Richard E. Turner, Douglas Eck,, (Intern co-author), Workshop

You can find the full list of accepted papers at ICLR 2017 here:

Edit: Added intern identification to one of the papers

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Natural and man-made colors

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#elastic stack 5.1.1 released

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#MXNet is the primary choice of Deep Learning framework at AWS

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"The rise of fake news has weaponized those filter bubbles. There were just too many people voting in this election because they were infuriated by made-up things they read online."

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