Conference on the Economics of Machine Intelligence-Dec 15

The Creative Destruction Lab at the University of Toronto is hosting a conference on the Economics of Machine Intelligence on December 15 in Toronto: “Machine Learning and the Market for Intelligence.”

This meeting is not a computer science conference. The focus is on the business opportunities that ML is spawning: what has already happened, trends, […]

Open Discussion of ICLR 2016 Papers is Now Open

Open discussion of #ICLR2016 submissions is now open:

https://cmt.research.microsoft.com/ICLR2016Conference/Protected/PublicComment.aspx

Access requires a CMT account. If you don’t have one already, go here:

https://cmt.research.microsoft.com/ICLR2016Conference/Public/SignUp.aspx

Note that the assigned reviewers and area chair of each paper will be encouraged to consider the public comments in their evaluation of submissions. Your comments will thus be very useful and […]

A Brief Summary of the Panel Discussion at DL Workshop @ICML 2015 by Kyunghyun Cho

You can also access original post from Kyunghyun Cho’s blog, DeepRNN .

Overview

The finale of the Deep Learning Workshop at ICML 2015 was the panel discussion on the future of deep learning. After a couple of weeks of extensive discussion and exchange of emails among the workshop organizers, we invited six panelists; Yoshua […]

Recent Reddit AMA’s about Deep Learning

Recently Geoffrey Hinton, Yann Lecun and Yoshua Bengio had reddit AMA’s where subscribers of r/MachineLearning asked questions to them. Each AMA contains interesting anectodes about deep learning by the most prominent scientists of the field.

Yoshua Bengio’s reddit AMA: http://www.reddit.com/r/MachineLearning/comments/1ysry1/ama_yoshua_bengio

Yann Lecun’s reddit AMA: http://www.reddit.com/r/MachineLearning/comments/25lnbt/ama_yann_lecun

Geoffrey Hinton’s AMA: http://www.reddit.com/r/MachineLearning/comments/2lmo0l/ama_geoffrey_hinton

In addition to those, Michael I […]

Call For Papers: ICLR 2015

3rd International Conference on Learning Representations (ICLR2015)

Website: http://www.iclr.cc/ Submission deadline: December 19, 2014 Location: Hilton San Diego Resort & Spa, May 7-9, 2015

Overview

It is well understood that the performance of machine learning methods is heavily dependent on the choice of data representation (or features) on which they are applied. The rapidly developing […]

ICLR 2014 Videos are available online

International Conference on Learning Representations (ICLR 2014) held between 14th of April and 16th of April in Banff with great interest from the deep learning community. The videos of talks are made available by the organizers at a Youtube channel[1].

[1]Youtube channel for ICLR 2014, https://www.youtube.com/playlist?list=PLhiWXaTdsWB-3O19E0PSR0r9OseIylUM8

Google’s new Deep Learning Algorithm Transcribes House Numbers

During his summer internship, Ian Goodfellow (currently a PhD student at UdeM Lisa Lab) and his collaborators from Google, Yaroslav Bulatov, Julian Ibarz, Sacha Arnoud, Vinay Shet, submitted a paper to ICLR 2014 that proposes a deep learning method which successfully transcribes the house numbers from Google Streetview images. This work took wide coverage in […]

ICLR 2014 Submissions are Open for Comments

ICLR2014 submissions are open for comments/reviews on OpenReview.net: http://openreview.net/venue/iclr2014. ICLR is the International Conference on Learning Representations. It uses an post-publication open review system. There are lots of interesting new work on deep learning and feature learning in there. Please make comments and contribute to making the submissions better[1].

[1] Yann LeCun’s Google+ and Facebook […]

Yoshua Bengio’s talk at MSR about Deep Learning of Representations and GSNs

Yoshua Bengio gave a recent presentation on “Deep Learning of Representation” and Generative Stochastic Networks (GSNs) at MSR and AAAI 2013. Slides of the talk can be accessed from this link.

ICLR 2013 submissions are open for Public discussion

Papers submitted to ICLR 2013 conference are open to public discussion. Please feel free to add your comments and share your thoughts about the papers. Link.