The QUIC transport protocol: design and Internet-scale deployment Langley et al., SIGCOMM’17 QUIC is a transport protocol designed from the ground up by Google improve the performance of HTTPS traffic. The chances are you’ve already used it - QUIC is deployed in Chrome, in the YouTube mobile app, and in the Google Search app on … Continue reading The QUIC transport protocol: design and Internet-scale deployment
Tag: Google
Google technology and systems.
TFX: A TensorFlow-based production scale machine learning platform
TFX: A TensorFlow-based production scale machine learning platform Baylor et al., KDD'17 What world-class looks like in online product and service development has been undergoing quite the revolution over the last few years. The series of papers we've been looking at recently can help you to understand where the bar is (it will have moved … Continue reading TFX: A TensorFlow-based production scale machine learning platform
Google Vizier: A service for black-box optimization
Google Vizier: a service for black-box optimization Golovin et al., KDD'17 We finished up last week by looking at the role of an internal (or external) experimentation platform. In today's paper Google remind us that such experimentation is just one form of optimisation. Google Vizier is an internal Google service for optimising pretty much anything. … Continue reading Google Vizier: A service for black-box optimization
BBR: Congestion-based congestion control
BBR: Congestion-based congestion control Cardwell et al., ACM Queue Sep-Oct 2016 With thanks to Hossein Ghodse (@hossg) for recommending today's paper selection. This is the story of how members of Google's make-tcp-fast project developed and deployed a new congestion control algorithm for TCP called BBR (for Bandwidth Bottleneck and Round-trip propagation time), leading to 2-25x … Continue reading BBR: Congestion-based congestion control
Shasta: Interactive reporting at scale
Shasta: Interactive Reporting At Scale Manoharan et al., SIGMOD 2016 You have vast database schemas with hundreds of tables, applications that need to combine OLTP and OLAP functionality, queries that may join 50 or more tables across disparate data sources, oh, and the user is waiting, so you'd better deliver the results online with low … Continue reading Shasta: Interactive reporting at scale
Strategic attentive writer for learning macro-actions
Strategic attentive writer for learning macro-actions Vezhnevets et al. (Google DeepMind), NIPS 2016 Baldrick may have a cunning plan, but most Deep Q Networks (DQNs) just react to what's immediately in front of them and what has come before. That is, at any given time step they propose the best action to take there and … Continue reading Strategic attentive writer for learning macro-actions
Unsupervised learning of 3D structure from images
Unsupervised learning of 3D structure from images Unsupervised learning of 3D structure from images Rezende et al. (Google DeepMind) NIPS,2016 Earlier this week we looked at how deep nets can learn intuitive physics given an input of objects and the relations between them. If only there was some way to look at a 2D scene … Continue reading Unsupervised learning of 3D structure from images
Matching networks for one shot learning
Matching networks for one shot learning Vinyals et al. (Google DeepMind), NIPS 2016 Yesterday we saw a neural network that can learn basic Newtonian physics. On reflection that's not totally surprising since we know that deep networks are very good at learning functions of the kind that describe our natural world. Alongside an intuitive understanding … Continue reading Matching networks for one shot learning
Interaction networks for learning about objects, relations and physics
Interaction networks for learning about objects, relations and physics Google DeepMind, NIPS 2016 Welcome back! There were so many great papers from OSDI '16 to cover at the end of last year that I didn't have a chance to get to NIPS. I'm kicking off this year therefore with a few of the Google DeepMind … Continue reading Interaction networks for learning about objects, relations and physics
TensorFlow: A system for large-scale machine learning
TensorFlow: A system for large-scale machine learning Abadi et al. (Google Brain) OSDI 2016 This is my last paper review for 2016! The Morning Paper will be taking a two week break for the holidays, resuming again on the 2nd January. Sometime inbetween I’ll do a short retrospective on the year. It seems fitting to … Continue reading TensorFlow: A system for large-scale machine learning
the morning paper 