Скачать или смотреть Clement Farabet, Senior Software Engineer, Twitter - RE•WORK Deep Learning Summit 2016

This presentation took place at the RE•WORK Deep Learning Summit in San Francisco on 28-29 January 2016: https://re-work.co/events/deep-learni...

Deep Learning at Twitter

Twitter is a unique source of real-time information, offering amazing opportunities for automatic content understanding. The format of this content is diverse (tweets, photos, videos, music, hyperlinks, follow graph, ...), the distribution of topics ever-changing (on a weekly, daily, or sometimes hourly basis), and the volume ever-growing; making it very challenging to automatically and continuously expose relevant content. Manually defining features to represent this data is showing its limits. In this talk, I provide an overview of how automated, content-driven representations—enabled by modern deep-learning algorithms—enables us to build adaptive systems which capture the richness of this content. Specifically, the presentation focuses on deep representations for images and images+text.

Clement Farabet is a senior software engineer at Twitter, where he leads the effort on representation learning for all things Twitter. Clement Farabet received a Master’s Degree in Electrical Engineering with honors from Institut National des Sciences Appliquées (INSA) de Lyon, France in 2008. His Master’s thesis work on reconfigurable hardware for deep neural networks was developed at the Courant Institute of Mathematical Sciences of New York University with Professor Yann LeCun, and led to a patent. He then joined Professor Yann LeCun’s laboratory in 2008, as a research scientist. In 2009, he started collaborating with Yale University’s e-Lab, led by Professor Eugenio Culurciello. This joint work later led to the creation of TeraDeep (www.teradeep.com). In 2010, he started the PhD program at Université Paris-Est, co-advised by Professors Laurent Najman and Yann LeCun. His thesis focused on real-time image understanding/parsing with deep convolutional networks. The main contributions of his thesis were multi-scale convolutional networks and graph-based techniques for efficient segmentations of class prediction maps. He graduated in 2013, and went on to cofound Madbits, a company that focused on representing, understanding and connecting images. Madbits got acquired by Twitter in 2014.