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
Author: adriancolyer
Venture Partner with Accel in London, author of 'The Morning Paper'
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
Panoply: Low-TCB Linux applications with SGX enclaves
Panoply: Low-TCB Linux applications with SGX enclaves Shinde et al., NDSS, 2017 Intel's Software Guard Extensions (SGX) supports a kind of reverse sandbox. With the normal sandbox model you're probably used to, we download untrusted code and run it in a trusted environment that we control. SGX supports running trusted code that you wrote, but … Continue reading Panoply: Low-TCB Linux applications with SGX enclaves
Enlightening the I/O path: A holistic approach for application performance
Enlightening the I/O Path: A holistic approach for application performance Kim et al., FAST '17 Lots of applications contain a mix of foreground and background tasks. Since we're at the file system level here, for application, think Redis, MongoDB, PostgreSQL and so on. Typically user requests are considered foreground tasks, and tasks such as housekeeping, … Continue reading Enlightening the I/O path: A holistic approach for application performance
Chronix: Long term storage and retrieval technology for anomaly detection in operational data
Chronix: Long term storage and retrieval technology for anomaly detection in operational data Lautenschlager et al., FAST 2017 Chronix (http://www.chronix.io/ ) is a time-series database optimised to support anomaly detection. It supports a multi-dimensional generic time series data model and has built-in high level functions for time series operations. Chronix also a scheme called "Date-Delta-Compaction" (DDC) … Continue reading Chronix: Long term storage and retrieval technology for anomaly detection in operational data
MaMaDroid: Detecting Android malware by building Markov chains of behavorial models
MaMaDroid: Detecting Android malware by building Markov chains of behavioral models, Mariconti et al., NDSS 2017 Pick any security conference of your choosing, and you're sure to find plenty of papers examining the security of Android. It can paint a pretty bleak picture, but at the same time the Android ecosystem also seems to have … Continue reading MaMaDroid: Detecting Android malware by building Markov chains of behavorial models
the morning paper 