Great! I will be at the lecture hall 15 minutes prior to the tea talk. If anyone brings the recording equipment, please, stop by and test the setting with me! On Fri, Aug 1, 2014 at 11:51 AM, Bing Xu <antinucleon@gmail.com> wrote:

I will take my camera. It is able to record 1.5 hour in 1080i. On Aug 1, 2014 11:49 AM, "Guillaume Alain" < guillaume.alain.umontreal@gmail.com> wrote:

...

One easy option is to have the speaker wear the kind of microphone/headphones combination that people use to talk on their phone. Then you can use any phone's voice recording function to record the audio and it should do a good job.

On Fri, Aug 1, 2014 at 11:45 AM, Sina Honari <sina.honari@gmail.com> wrote:

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another mobile or a laptop can be used next to Yoshua for just recording the voice. If the camera is away the voice shouldn't be that good.

On 1 August 2014 11:38, Tae-Ho Kim <ktho894@gmail.com> wrote:

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I'll also bring my camera as well. I've never tried recording for long time, i hope that it works.

On Aug 1, 2014 11:26 AM, "Kyung Hyun Cho" <cho.k.hyun@gmail.com> wrote:

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Tapani will record the talk with his laptop. It's not going to of

super

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quality, but hopefully will be good enough to hear and see the talk.

On Fri, Aug 1, 2014 at 10:06 AM, Kyung Hyun Cho <cho.k.hyun@gmail.com

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wrote:

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Unfortunately, I don't have anything to record the talk with. Is

there

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anyone else at the lab who has happened to bring their camcorder or camera that can record the talk?

On Fri, Aug 1, 2014 at 9:51 AM, KyoungGu Woo <epigramwoo@gmail.com> wrote: > > That's a nice idea. > I would also be benefited a lot. > > Kyoung-Gu > > 2014. 8. 1. 오전 9:37에 "Pierre Luc Carrier" > <carrier.pierreluc@gmail.com>님이 작성: > >> I cannot make it but I would be very interested in seeing this >> tea-talk. If others are in the same situation, perhaps we could look into >> recording this tea-talk like we did with guillaume's. I do not know if we >> have the material to do this in the lab, though. >> >> Pierre Luc >> >> >> 2014-07-31 17:11 GMT-04:00 Yoshua Bengio <yoshua.bengio@gmail.com : >>> >>> Please ignore the attached pdf, it is a very old version. The arxiv >>> version is much better, with many mistakes fixed: >>> >>> http://arxiv.org/abs/1407.7906 >>> >>> In the future, of course, the svn version will always be the latest >>> one (articles/2014/targetprop). >>> >>> -- Yoshua >>> >>> >>> On Thu, Jul 31, 2014 at 4:49 PM, Kyung Hyun Cho < cho.k.hyun@gmail.com> >>> wrote: >>>> >>>> Dear all, >>>> >>>> We will have a tea talk this Friday by Prof. Yoshua Bengio. See below >>>> for the details and the attached paper. >>>> >>>> Hope to see many of you there! >>>> - Cho >>>> >>>> === >>>> - Speaker: Prof. Yoshua Bengio (University of Montreal) >>>> - Date and Time: 1 Aug 2014 @13.00 >>>> - Place: AA3195 >>>> - Title: How Auto-Encoders Could Provide Credit Assignment in Deep >>>> Networks via Target Propagation >>>> - Abstract: >>>> In this paper we propose to exploit reconstruction as a layer-local >>>> training signal for deep learning, be it generative or discriminant, single >>>> or multi-modal, supervised, semi-supervised or unsupervised, feedforward or >>>> recurrent. Reconstructions can be propagated in a form of target propagation >>>> playing a role similar to back-propagation but helping to reduce the >>>> reliance on back-propagation in order to perform credit assignment across >>>> many levels of possibly strong non-linearities (which is difficult for >>>> back-propagation). A regularized auto-encoder tends produce a reconstruction >>>> that is a more likely version of its input, i.e., a small move in the >>>> direction of higher likelihood. By generalizing gradients, target >>>> propagation may also allow to train deep networks with discrete hidden >>>> units. If the auto-encoder takes both a representation of input and target >>>> (or of any side information) in input, then its reconstruction of input >>>> representation provides a target towards a representation that is more >>>> likely, conditioned on all the side information. A deep auto-encoder >>>> decoding path generalizes gradient propagation in a learned way that can >>>> thus handle not just infinitesimal changes but larger, discrete changes, >>>> hopefully allowing credit assignment through a long chain of non-linear >>>> operations. For this to work, each layer must be a good denoising or >>>> regularized auto-encoder itself. In addition to each layer being a good >>>> auto-encoder, the encoder also learns to please the upper layers by >>>> transforming the data into a space where it is easier to model by them, >>>> flattening manifolds and disentangling factors. The motivations and >>>> theoretical justifications for this approach are laid down in this paper, >>>> along with conjectures that will have to be verified either mathematically >>>> or experimentally. >>>> >>>> _______________________________________________ >>>> Lisa_labo mailing list >>>> Lisa_labo@iro.umontreal.ca >>>> https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo >>>> >>> >>> >>> _______________________________________________ >>> Lisa_labo mailing list >>> Lisa_labo@iro.umontreal.ca >>> https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo >>> >> >> >> _______________________________________________ >> Lisa_labo mailing list >> Lisa_labo@iro.umontreal.ca >> https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo >> > > _______________________________________________ > Lisa_labo mailing list > Lisa_labo@iro.umontreal.ca > https://webmail.iro.umontreal.ca/mailman/listinfo/lisa_labo >

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