Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples Athalye et al., ICML'18 There has been a lot of back and forth in the research community on adversarial attacks and defences in machine learning. Today’s paper examines a number of recently proposed defences and shows that most of them rely on … Continue reading Obfuscated gradients give a false sense of security: circumventing defenses to adversarial examples
Tag: Deep Learning
The deep learning subset of machine learning.
Deep code search
Deep code search Gu et al., ICSE'18 The problem with searching for code is that the query, e.g. "read an object from xml," doesn’t look very much like the source code snippets that are the intended results, e.g.: * That’s why we have Stack Overflow! Stack Overflow can help with ‘how to’ style queries, but … Continue reading Deep code search
DeepTest: automated testing of deep-neural-network-driven autonomous cars
DeepTest: automated testing of deep-neural-network-driven autonomous cars Tian et al., ICSE'18 How do you test a DNN? We’ve seen plenty of examples of adversarial attacks in previous editions of The Morning Paper, but you couldn’t really say that generating adversarial images is enough to give you confidence in the overall behaviour of a model under … Continue reading DeepTest: automated testing of deep-neural-network-driven autonomous cars
Optimus: an efficient dynamic resource scheduler for deep learning clusters
Optimus: an efficient dynamic resource scheduler for deep learning clusters Peng et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). It’s another paper promising to reduce your deep learning training times today. But instead of … Continue reading Optimus: an efficient dynamic resource scheduler for deep learning clusters
Improving the expressiveness of deep learning frameworks with recursion
Improving the expressiveness of deep learning frameworks with recursion Jeong, Jeong et al., EuroSys'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site). Last week we looked at the embedded dynamic control flow operators in TensorFlow. In today’s … Continue reading Improving the expressiveness of deep learning frameworks with recursion
Measuring the tendency of CNNs to learn surface statistical regularities
Measuring the tendency of CNNs to learn surface statistical regularities Jo et al., arXiv'17 With thanks to Cris Conde for bringing this paper to my attention. We’ve looked at quite a few adversarial attacks on deep learning systems in previous editions of The Morning Paper. I find them fascinating for what they reveal about the … Continue reading Measuring the tendency of CNNs to learn surface statistical regularities
Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications
Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications Xu et al., WWW'18 (If you don’t have ACM Digital Library access, the paper can be accessed either by following the link above directly from The Morning Paper blog site, or from the WWW 2018 proceedings page). Today’s paper examines the problem of … Continue reading Unsupervised anomaly detection via variational auto-encoder for seasonal KPIs in web applications
Progressive growing of GANs for improved quality, stability, and variation
Progressive growing of GANs for improved quality, stability, and variation Karras et al., ICLR'18 Let’s play "spot the celebrity"! (Not your usual #themorningpaper fodder I know, but bear with me...) In each row, one of these is a photo of a real person, the other image is entirely created by a GAN. But which is … Continue reading Progressive growing of GANs for improved quality, stability, and variation
Photo-realistic single image super-resolution using a generative adversarial network
Photo-realistic single image super-resolution using a generative adversarial network Ledig et al., arXiv'16 Today’s paper choice also addresses an image-to-image translation problem, but here we’re interested in one specific challenge: super-resolution. In super-resolution we take as input a low resolution image like this: And produce as output an estimation of a higher-resolution up-scaled version: For … Continue reading Photo-realistic single image super-resolution using a generative adversarial network
Image-to-image translation with conditional adversarial networks
Image-to-image translation with conditional adversarial networks Isola et al., CVPR’17 It’s time we looked at some machine learning papers again! Over the next few days I’ve selected a few papers that demonstrate the exciting capabilities being developed around images. I find it simultaneously amazing to see what can be done, and troubling to think about … Continue reading Image-to-image translation with conditional adversarial networks
the morning paper 