Neural architecture search with reinforcement learning

Neural architecture search with reinforcement learning Zoph & Le, ICLR'17 Earlier this year we looked at 'Large scale evolution of image classifiers' which used an evolutionary algorithm to guide a search for the best network architectures. In today's paper, Zoph & Le also demonstrate that learning network architectures (and also in their case recurrent cell … Continue reading Neural architecture search with reinforcement learning

Semi-supervised knowledge transfer for deep learning from private training data

Semi-supervised knowledge transfer for deep learning from private training data Papernot et al., ICLR'17 How can you build deep learning models that are trained on sensitive data (e.g., concerning individuals), and be confident to deploy those models in the wild knowing that they won't leak any information about the individuals in the training set? As … Continue reading Semi-supervised knowledge transfer for deep learning from private training data

End-to-end optimized image compression

End-to-end optimized image compression BallĂ© et al., ICLR'17 We'll be looking at some of the papers from ICLR'17 this week, starting with "End-to-end optimized image compression" which makes a nice comparison with the Dropbox JPEG compression paper that we started with last week. Of course you won't be surprised to find out that the authors … Continue reading End-to-end optimized image compression

A miscellany of fun deep learning papers

To round out the week, I thought I'd take a selection of fun papers from the 'More papers from 2016' section of top 100 awesome deep learning papers list. Colorful image colorization, Zhang et al., 2016 Texture networks: feed-forward synthesis of textures and stylized images Generative visual manipulation on the natural image manifold, Zhu et … Continue reading A miscellany of fun deep learning papers