Large-scale evolution of image classifiers Real et al., 2017 I'm sure you noticed the bewildering array of network architectures in use when we looked at some of the top convolution neural network papers of the last few years last week (Part 1, Part2, Part 3). With sufficient training data, these networks can achieve amazing feats, … Continue reading Large-scale evolution of image classifiers
Year: 2017
Ethically aligned design
The IEEE Global Initiative for Ethical Considerations in Artificial Intelligence and Autonomous Systems. Ethically Aligned Design: A Vision For Prioritizing Wellbeing With Artificial Intelligence And Autonomous Systems, Version 1. IEEE, 2016. http://standards.ieee.org/develop/indconn/ec/autonomous_systems.html. Something a little different for today... the IEEE recently put out a first version of their "Ethically Aligned Design" report for public … Continue reading Ethically aligned design
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
Recurrent Neural Network models
Today we're pressing on with the top 100 awesome deep learning papers list, and the section on recurrent neural networks (RNNs). This contains only four papers (joy!), and even better we've covered two of them previously (Neural Turing Machines and Memory Networks, the links below are to the write-ups). That leaves up with only two … Continue reading Recurrent Neural Network models
Convolution neural networks, Part 3
Today we're looking at the final four papers from the 'convolutional neural networks' section of the 'top 100 awesome deep learning papers' list. Deep residual learning for image recognition, He et al., 2016 Identity mappings in deep residual networks, He et al., 2016 Inception-v4, inception-resnet and the impact of residual connections or learning, Szegedy et … Continue reading Convolution neural networks, Part 3
Convolution neural nets, Part 2
Today it's the second tranche of papers from the convolutional neural nets section of the 'top 100 awesome deep learning papers' list: Return of the devil in the details: delving deep into convolutional nets, Chatfield et al., 2014 Spatial pyramid pooling in deep convolutional networks for visual recognition, He et al., 2014 Very deep convolutional … Continue reading Convolution neural nets, Part 2
Convolutional neural networks, Part 1
Having recovered somewhat from the last push on deep learning papers, it's time this week to tackle the next batch of papers from the 'top 100 awesome deep learning papers.' Recall that the plan is to cover multiple papers per day, in a little less depth than usual per paper, to give you a broad … Continue reading Convolutional neural networks, Part 1
Omid reloaded: scalable and highly-available transaction processing
Omid, reloaded: scalable and highly-available transaction processing Shacham et al., FAST '17 Omid is a transaction processing service powering web-scale production systems at Yahoo that digest billions of events per day and push them into a real-time index. It's also been open-sourced and is currently incubating at Apache as the Apache Omid project. What's interesting … Continue reading Omid reloaded: scalable and highly-available transaction processing
Deconstructing Xen
Deconstructing Xen Shi et al., NDSS 2017 Unfortunately, one of the most widely-used hypervisors, Xen, is highly susceptible to attack because it employs a monolithic design (a single point of failure) and comprises a complex set of growing functionality including VM management, scheduling, instruction emulation, IPC (event channels), and memory management. As of v4.0, Xen … Continue reading Deconstructing Xen
Application crash consistency and performance with CCFS
Application crash consistency and performance with CCFS Pillai et al., FAST 2017 I know I tend to get over-excited about some of the research I cover, but this is truly a fabulous piece of work. We looked "All file systems are not created equal" in a previous edition of The Morning Paper, which showed that … Continue reading Application crash consistency and performance with CCFS
the morning paper 