[Lisa_teatalk] [Lisa_labo] Tea Talk 1 Aug Friday @13.00 AA3195 by Yoshua Bengio

Kyung Hyun Cho cho.k.hyun at gmail.com
Fri Aug 1 11:58:26 EDT 2014


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 at 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 at 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 at gmail.com>
>> wrote:
>>
>>> 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 at gmail.com> wrote:
>>> > 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 at gmail.com>
>>> wrote:
>>> >>
>>> >> Tapani will record the talk with his laptop. It's not going to of
>>> super
>>> >> 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 at gmail.com
>>> >
>>> >> wrote:
>>> >>>
>>> >>> Unfortunately, I don't have anything to record the talk with. Is
>>> there
>>> >>> 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 at 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 at 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 at 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 at 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.
>>> >>>>>>>
>>> >>>>>>> _______________________________________________
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