Typed Architectures: architectural support for lightweight scripting Kim et al., ASPLOS'17 JavaScript, Python, Ruby, Lua, and related dynamically typed scripting languages are increasingly popular for developing IoT applications. For example, the Raspberry Pi is associated with Python; Arduino and Intel's Galileo and Edison are associated with JavaScript. In these constrained hardware environments though, using JITs … Continue reading Typed Architectures: architectural support for lightweight scripting
Year: 2017
Who controls the Internet? Analyzing global threats using property traversal graphs
Who controls the Internet? Analyzing global threats using property traversal graphs Simeonovski et al., WWW'17 Who controls the Internet? How much influence do they have? And what would happen if one of those parties launched an attack or was compromised and used to launch an attack? Previous works have looked at the individual core services, … Continue reading Who controls the Internet? Analyzing global threats using property traversal graphs
BOAT: Building auto-tuners with structured Bayesian optimization
BOAT: Building auto-tuners with structured Bayesian optimization Dalibard et al., WWW'17 Due to their complexity, modern systems expose many configuration parameters which users must tune to maximize performance... From the number of machines used in a distributed application, to low-level parameters such as compiler flags, managing configurations has become one of the main challenges faced … Continue reading BOAT: Building auto-tuners with structured Bayesian optimization
DeepSense: a unified deep learning framework for time-series mobile sensing data processing
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
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
the morning paper 