DeepSense: a unified deep learning framework for time-series mobile sensing data processing Yao et al., WWW'17 DeepSense is a deep learning framework that runs on mobile devices, and can be used for regression and classification tasks based on data coming from mobile sensors (e.g., motion sensors). An example of a classification task is heterogeneous human … Continue reading DeepSense: a unified deep learning framework for time-series mobile sensing data processing
Month: May 2017
Usage patterns and the economics of the public cloud
Usage patterns and the economics of the public cloud Kilcioglu et al., WWW'17 Illustrating the huge diversity of topics covered at WWW, following yesterday's look at recovering mobile user trajectories from aggregate data, today's choice studies usage variation and pricing models in the public cloud. The basis for the study is data from 'a major … Continue reading Usage patterns and the economics of the public cloud
Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data
Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data Xu et al., WWW'17 Borrowing a little from Simon Wardley's marvellous Enterprise IT Adoption Cycle, here's roughly how my understanding progressed as I read through this paper: Huh? What? How? Nooooo, Oh No, Oh s*@\#! Xu et al. show us that even … Continue reading Trajectory recovery from Ash: User privacy is NOT preserved in aggregated mobility data
Learning to act by predicting the future
Learning to act by predicting the future Dosovitskiy & Koltun, ICLR'17 The Direct Future Prediction (DFP) model won the 'Full Deathmatch' track of the Visual Doom AI Competion in 2016. The competition pits agents against each other, with their performance measured by how many 'frags' they get. (A frag is a kill, apparently - not … Continue reading Learning to act by predicting the future
Understanding deep learning requires re-thinking generalization
Understanding deep learning requires re-thinking generalization Zhang et al., ICLR'17 This paper has a wonderful combination of properties: the results are easy to understand, somewhat surprising, and then leave you pondering over what it all might mean for a long while afterwards! The question the authors set out to answer was this: What is it … Continue reading Understanding deep learning requires re-thinking generalization
Neural architecture search with reinforcement learning
Neural architecture search with reinforcement learning Zoph & Le, ICLR'17 Earlier this year we looked at 'Large scale evolution of image classifiers' which used an evolutionary algorithm to guide a search for the best network architectures. In today's paper, Zoph & Le also demonstrate that learning network architectures (and also in their case recurrent cell … Continue reading Neural architecture search with reinforcement learning
Semi-supervised knowledge transfer for deep learning from private training data
Semi-supervised knowledge transfer for deep learning from private training data Papernot et al., ICLR'17 How can you build deep learning models that are trained on sensitive data (e.g., concerning individuals), and be confident to deploy those models in the wild knowing that they won't leak any information about the individuals in the training set? As … Continue reading Semi-supervised knowledge transfer for deep learning from private training data
End-to-end optimized image compression
End-to-end optimized image compression Ballé et al., ICLR'17 We'll be looking at some of the papers from ICLR'17 this week, starting with "End-to-end optimized image compression" which makes a nice comparison with the Dropbox JPEG compression paper that we started with last week. Of course you won't be surprised to find out that the authors … Continue reading End-to-end optimized image compression
Efficient memory disaggregation with Infiniswap
Efficient memory disaggregation with Infiniswap Gu et al., NSDI '17 If we move performance numbers onto a human scale (let 1ns of processor time = 1 second of human time) then it's easier to get an intuition - for me at least - of the relative cost of different operations. In this world, it takes … Continue reading Efficient memory disaggregation with Infiniswap
CherryPick: Adaptively unearthing the best cloud configurations for big data analytics
CherryPick: Adaptively unearthing the best cloud configurations for big data analytics Alipourfard et al., NSDI'17 For big data analytics jobs, especially recurring jobs, finding a good cloud configuration (number and type of machines, CPU, memory ,disk and network options) can make a big different to overall cost and runtimes. Likewise, a poor choice can seriously … Continue reading CherryPick: Adaptively unearthing the best cloud configurations for big data analytics
the morning paper 