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
Category: Uncategorized
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
Polaris: Faster Page Loads Using Fine-Grained Dependency Tracking
Polaris: Faster Page Loads Using Fine-Grained Dependency Tracking - Netravali et al. 2016 Yesterday we looked at Shandian which promised faster web page load times, but required a modified client-side browser. Today we're sticking with the theme of reducing page load times with Polaris. Unlike Shandian, Polaris works with unmodified browsers, and in tests with … Continue reading Polaris: Faster Page Loads Using Fine-Grained Dependency Tracking
Speeding up Web Page Loads with Shandian
Speeding up Web Page Loads with Shandian - Wang et al. 2016 Despite its importance and various attempts to improve page load time (PLT), the end-to-end PLT for most pages is still a few seconds on desktops and more than ten seconds on mobile devices. Page load times are very important for user experience and … Continue reading Speeding up Web Page Loads with Shandian
Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds
Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds - Wang et al. 2016 Who owns your data? With cloud services, 'your' data is typically spread across multiple walled gardens, one per service. I'm reminded of a great line from "On the duality of resilience and privacy:" It is a truth universally acknowledged … Continue reading Sieve: Cryptographically Enforced Access Control for User Data in Untrusted Clouds
the morning paper 