The Linux Scheduler: a Decade of Wasted Cores - Lozi et al. 2016 This is the first in a series of papers from EuroSys 2016. There are three strands here: first of all, there's some great background into how scheduling works in the Linux kernel; secondly, there's a story about Software Aging and how changing … Continue reading The Linux Scheduler: a Decade of Wasted Cores
Year: 2016
Delta State Replicated Data Types
Delta State Replicated Data Types - Almeida et al. 2016 You know when you want to use CRDTs for their convergence properties, but the amount of state you're required to pass around gets out of hand? In this paper, Almeida et al. show how to retain the advantages of state-based CRDTs, but with much smaller … Continue reading Delta State Replicated Data Types
GloVe: Global Vectors for Word Representation
GloVe: Global Vectors for Word Representation - Pennington et al. 2014 Yesterday we looked at some of the amazing properties of word vectors with word2vec. Pennington et al. argue that the online scanning approach used by word2vec is suboptimal since it doesn't fully exploit statistical information regarding word co-occurrences. They demonstrate a Global Vectors (GloVe) … Continue reading GloVe: Global Vectors for Word Representation
The amazing power of word vectors
For today's post, I've drawn material not just from one paper, but from five! The subject matter is 'word2vec' - the work of Mikolov et al. at Google on efficient vector representations of words (and what you can do with them). The papers are: Efficient Estimation of Word Representations in Vector Space - Mikolov et … Continue reading The amazing power of word vectors
ImageNet Classification with Deep Convolutional Neural Networks
ImageNet Classification with Deep Convolutional Neural Networks - Krizhevsky et al. 2012 Like the large-vocabulary speech recognition paper we looked at yesterday, today's paper has also been described as a landmark paper in the history of deep learning. It's also a surprisingly easy read! The ImageNet dataset contains over 15 million labeled high-resolution images of … Continue reading ImageNet Classification with Deep Convolutional Neural Networks
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition - Dahl et al. 2011 The title may be a bit of a mouthful, but this paper is often cited as a watershed moment for deep learning and speech recognition. It represents the first application of deep neural networks for large vocabulary speech recognition (LVSR), and … Continue reading Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
Deep Learning in Neural Networks: An Overview
Deep Learning in Neural Networks: An Overview - Schmidhuber 2014 What a wonderful treasure trove this paper is! Schmidhuber provides all the background you need to gain an overview of deep learning (as of 2014) and how we got there through the preceding decades. Starting from recent DL results, I tried to trace back the … Continue reading Deep Learning in Neural Networks: An Overview
End of term
Hard to believe we're at the end of March already! The Morning Paper is taking a two week break for the end of term. Paper summaries will resume again on Monday 18th April. I'll be using the time to top up my paper backlogs, and brush up on my linear algebra. Computer Science has a … Continue reading End of term
Scalable and private media consumption with Popcorn
Scalable and private media consumption with Popcorn - Gupta et al. 2016 What price can we put on privacy? For streaming media consumption (think Netflix) in which you have complete privacy concerning the media you are watching (i.e., not even the service provider knows - how is this even possible? We'll get to that...), it … Continue reading Scalable and private media consumption with Popcorn
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics
Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics - Venkataraman et al. 2016 With cloud computing environments such as Amazon EC2, users typically have a large number of choices in terms of the instance types and number of instances they can run their jobs on. Not surprisingly, the amount of memory per core, storage media, … Continue reading Ernest: Efficient Performance Prediction for Large-Scale Advanced Analytics
the morning paper 