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
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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
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
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
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
Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions
Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions Ganesan et al., FAST 2017 It's a tough life being the developer of a distributed datastore. Thanks to the wonderful work of Kyle Kingsbury (aka, @aphyr) and his efforts on Jepsen.io, awareness of data loss and related issues in … Continue reading Redundancy does not imply fault tolerance: analysis of distributed storage reactions to single errors and corruptions
Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web
Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web Lauinger et al., NDSS 2017 Just based on the paper title alone, if you had to guess what the situation is with outdated JavaScript libraries on the web, you'd probably guess it was pretty bad. It turns out it's … Continue reading Thou shalt not depend on me: analysing the use of outdated JavaScript libraries on the web
HopFS: Scaling hierarchical file system metadata using NewSQL databases
HopFS: Scaling hierarchical file system metadata using NewSQL databases Niazi et al., FAST 2017 If you're working with big data and Hadoop, this one paper could repay your investment in The Morning Paper many times over (ok, The Morning Paper is free - but you do pay with your time to read it). You know … Continue reading HopFS: Scaling hierarchical file system metadata using NewSQL databases