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

Sina Honari sina.honari at gmail.com
Fri Aug 1 11:45:06 EDT 2014


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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>>>>>>> Lisa_labo at iro.umontreal.ca
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>>>>>>>
>>>>>>
>>>>>>
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>>>>>
>>>>>
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