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

Yoshua Bengio yoshua.bengio at gmail.com
Fri Aug 1 11:34:21 EDT 2014


Some phones have a pretty good camera. The only problem is that the default
memory card is barely enough for 1 hour of high-quality video, assuming you
don't have other things on the phone that take Gigabytes of space. I
probably have enough space for about half an hour on my phone.

-- Yoshua


On Fri, Aug 1, 2014 at 11:24 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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>>>>>>
>>>>>
>>>>> _______________________________________________
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>>>>>
>>>>
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>>
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