Dynamic routing between capsules Sabour et al., NIPS’17 The Morning Paper isn’t trying to be a ‘breaking news’ site (there are plenty of those already!) — we covered a paper from 1950 last month for example! That said, when exciting research news breaks, of course I’m interested to read up on it. So The Morning … Continue reading Dynamic routing between capsules
Tag: Deep Learning
The deep learning subset of machine learning.
DeepXplore: automated whitebox testing of deep learning systems
DeepXplore: automated whitebox testing of deep learning systems Pei et al., SOSP’17 The state space of deep learning systems is vast. As we’ve seen with adversarial examples, that creates opportunity to deliberately craft inputs that fool a trained network. Forget adversarial examples for a moment though, what about the opportunity for good old-fashioned bugs to … Continue reading DeepXplore: automated whitebox testing of deep learning systems
Distributed deep neural networks over the cloud, the edge, and end devices
Distributed deep neural networks over the cloud, the edge, and end devices Teerapittayanon et al., ICDCS 17 Earlier this year we looked at Neurosurgeon, in which the authors do a brilliant job of exploring the trade-offs when splitting a DNN such that some layers are processed on an edge device (e.g., mobile phone), and some … Continue reading Distributed deep neural networks over the cloud, the edge, and end devices
Accelerating innovation through analogy mining
Accelerating innovation through analogy mining Hope et al., KDD'17 Today's choice won a best paper award at KDD'17. It's a really interesting twist on information retrieval, building on a foundation of GloVe and word vectors to create purpose and mechanism vectors for a corpus of product descriptions. Using these vectors, the authors show how to … Continue reading Accelerating innovation through analogy mining
Adversarial examples for evaluating reading comprehension systems
Adversarial examples for evaluating reading comprehension systems Jia & Liang, EMNLP 2017 We've now seen a number of papers investigating adversarial examples for images. In today's paper choice, Jia and Liang explore adversarial examples for text samples in the context of reading comprehension systems. The results are frankly a bit of a wake-up call for … Continue reading Adversarial examples for evaluating reading comprehension systems
Universal adversarial perturbations
Universal adversarial perturbations Moosavi-Dezfooli et al., CVPR 2017. I'm fascinated by the existence of adversarial perturbations - imperceptible changes to the inputs to deep network classifiers that cause them to mis-predict labels. We took a good look at some of the research into adversarial images earlier this year, where we learned that all deep networks … Continue reading Universal adversarial perturbations
Learning transferable architectures for scalable image recognition
Learning transferable architectures for scalable image recognition Zoph et al., arXiv 2017 Things move fast in the world of deep learning! It was only a few months ago that we looked at 'Neural architecture search with reinforcement learning.' In that paper, Zoph et al., demonstrate that just like we once designed features by hand but … Continue reading Learning transferable architectures for scalable image recognition
Deep photo style transfer
Deep photo style transfer Luan et al., arXiv 2017 Here's something a little fun for Friday: a collaboration between researchers at Cornell and Adobe, on photographic style transfer. Will we see something like this in a Photoshop of the future? In 2015 in the Neural Style Transfer paper ('A neural algorithm of artistic style'), Gatys … Continue reading Deep photo style transfer
Cardiologist-level arrhythmia detection with convolutional neural networks
Cardiologist-level arrythmia detection with convolutional neural networks Rajpurkar, Hannun, et al., arXiv 2017 See also https://stanfordmlgroup.github.io/projects/ecg. This is a story very much of our times: development and deployment of better devices/sensors (in this case an iRhythm Zio) leads to collection of much larger data sets than have been available previously. Apply state of the art … Continue reading Cardiologist-level arrhythmia detection with convolutional neural networks
Neurosurgeon: collaborative intelligence between the cloud and the mobile edge
Neurosurgeon: collaborative intelligence between the cloud and mobile edge Kang et al., ASPLOS'17 For a whole class of new intelligent personal assistant applications that process images, videos, speech, and text using deep neural networks, the common wisdom is that you really need to run the processing in the cloud to take advantage of powerful clusters … Continue reading Neurosurgeon: collaborative intelligence between the cloud and the mobile edge
the morning paper 