[Lisa_seminaires] / Li Yao

Yoshua Bengio yoshua.umontreal at gmail.com
Mar 5 Déc 09:39:18 EST 2017


==> 15h-17h

2017-12-04 21:22 GMT-08:00 Yoshua Bengio <yoshua.umontreal at gmail.com>:

> Soutenance de thèse / PhD defense
>
> Lundi 11 décembre, local 3195 du Pavillon Aisenstadt
>
> Candidat: Li Yao
> Directeur: Yoshua Bengio
> Examinateur externe: Sanja Fidler
> Autres membres du jury: Aaron Courville, Christopher Pal
>
> Learning visual representations with neural networks for image generation
> and video captioning
>
> The past decade has been marked as a golden era of neural network
> research. Not only have neural networks been successfully applied to
> solve more and more challenging real-world problems, but also they
> have become the dominant approach in many of the places where they
> have been tested. These places include, for instance, language
> understanding, game playing, and computer vision, thanks to neural
> networks' superiority in computational efficiency and statistical
> capacity.
>
> This work applies neural networks to problems in computer vision where
> high-level and semantically meaningful representations play a
> fundamental role. It demonstrates both in theory and in experiment the
> ability to learn such representations from data with and without
> supervision.
>
> The main content of the work is divided into two parts. The first part
> studies neural networks in the context of learning visual
> representations for the task of video captioning. Models are developed
> to dynamically focus on different frames while generating a natural
> language description of a short video. Such a model is further
> improved by recurrent convolutional operations. The end of this part
> identifies fundamental challenges in video captioning and proposes a
> new type of model-based evaluation metric that may be used
> experimentally as an oracle to benchmark performance.
>
> The second part studies the family of models that generate images.
> While the first part is supervised, this part is unsupervised. The
> focus of it is the popular family of Neural Autoregressive Density
> Estimators (NADEs), a tractable probabilistic model for natural
> images. This work first makes a connection between NADEs and
> Generative Stochastic Networks (GSNs). The standard NADE is improved
> by introducing multiple iterations in its inference without increasing
> the number of parameters, which is dubbed "iterative NADEs".
>
>
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