1st Tuesday Journal-Paper Club: July 2015 meeting
This month's paper is:
Deep learning, Yann LeCun, Yoshua Bengio & Geoffrey Hinton, Nature 521, 436–444 (28 May 2015) doi:10.1038/nature14539
Kristian Weegink has kindly agreed to lead the discussion and the venue will again be the Brisbane Brewhouse (see address details & map below).
*About the 1st Tuesday Journal-Paper Club:* the idea is to meet regularly, usually on the 1st Tuesday of the month as the name suggests (inspired by the ABC TV series "1st Tuesday Book Club"). Each month, the participants would agree on a highly cited, 'top ten' or major-prize-winning article in an SPS or ComSoc journal (but not one of our own!). We would also select a Discussion Leader. Through the month, each of the participants would read the article. At the next meeting, the Discussion Leader would lead a discussion of that article, starting with his/her own appraisal. In this way, it is hoped that we could all broaden our understanding of the field and further develop a sense of community. 1st rule of 1st Tuesday Journal-Paper Club: tell everyone about 1st Tuesday Journal-Paper Club.
Date and Time
Location
Hosts
Registration
- Date: 07 Jul 2015
- Time: 06:00 PM to 08:00 PM
- All times are (UTC+10:00) Brisbane
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- 601 Stanley St.
- Woolloongabba, Queensland
- Australia 4102
- Building: Brewhouse Brisbane
- Starts 25 June 2015 06:00 AM
- Ends 06 July 2015 12:00 PM
- All times are (UTC+10:00) Brisbane
- No Admission Charge
Agenda
In this month's session we can discuss this very recent nature paper on deep learning AND the strategies utlised to publish engineering and computer science papers in high impact outlets, such as Nature.
Abstract
Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction. These methods have dramatically improved the state-of-the-art in speech rec- ognition, visual object recognition, object detection and many other domains such as drug discovery and genomics. Deep learning discovers intricate structure in large data sets by using the backpropagation algorithm to indicate how a machine should change its internal parameters that are used to compute the representation in each layer from the representation in the previous layer. Deep convolutional nets have brought about breakthroughs in processing images, video, speech and audio, whereas recurrent nets have shone light on sequential data such as text and speech.