Novembre 2013 - Lisa_teatalk - mailman.iro.umontreal.ca

Practice talk, Combining Modality Specific Deep Nets for Emotion Recognition in Video
by Razvan Pascanu 29 Nov '13

29 Nov '13

This is a practice talk that Samira will be giving at ICMI in Sydney, presenting the results of our team on the EmotiW challenge. It's a short presentation (20 minutes), and we would like you to be present and give her feedback. Title: Combining Modality Speciﬁc Deep Neural Networks for Emotion Recognition in Video Speaker: Samira Ebrahimi Kahou Location: AA-1409 Time: Tuesday, December 3rd, 3 p.m. Abstract of the article: In this paper we present the techniques used for the University of Montreal's team submissions to the 2013 Emotion Recognition in the Wild Challenge. The challenge is to classify the emotions expressed by the primary human subject in short video clips extracted from feature length movies. This involves the analysis of video clips of acted scenes lasting approximately one-two seconds, including the audio track which may contain human voices as well as background music. Our approach combines multiple deep neural networks for different data modalities, including: (1) a deep convolutional neural network for the analysis of facial expressions within video frames; (2) a deep belief net to capture audio information; (3) a deep autoencoder to model the spatio-temporal information produced by the human actions depicted within the entire scene; and (4) a shallow network architecture focused on extracted features of the mouth of the primary human subject in the scene. We discuss each of these techniques, their performance characteristics and different strategies to aggregate their predictions. Our best single model was a convolutional neural network trained to predict emotions from static frames using two large data sets, the Toronto Face Database and our own set of faces images harvested from Google image search, followed by a per frame aggregation strategy that used the challenge training data. This yielded a test set accuracy of 35.58%. Using our best strategy for aggregating our top performing models into a single predictor we were able to produce an accuracy of 41.03% on the challenge test set. These compare favorably to the challenge baseline test set accuracy of 27.56%. Hope to see many of you there, Razvan

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Tea talk this Friday 3PM
by Razvan Pascanu 19 Nov '13

19 Nov '13

Hi all, This week we have a tea talk where Caglar, Kyunghyun and myself will present the work submitted at AISTATS : Lp units for MLPs. I originally wanted to do this last week, but had to reschedule for this week. Also note that Friday 3PM will be the new tea-talk time (at least for now). The talk will try to go beyond what with submitted. We will try to summarize some ideas we have to extend this work, namely my allowing more flexibility in the units. I will also talk about how one can visualize what these units are doing and how that could be useful. The abstract of the paper: In this paper we proposed a novel nonlinear unit, which is called as Lp unit, for a multi-layer perceptron (MLP). The proposed Lp unit receives signal from several projections of the layer below and computes the normalized Lp norm. We notice two interesting interpretations of the Lp unit. First, we note that the proposed unit is a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks(CNN), HMAX models and neocognitrons. Furthermore, under certain constraints, the Lp unit is a generalization of the recently proposed maxout unit(Goodfellow et al, 2013) which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Second, we provide a geometrical interpretation of the activation function. Each Lp unit defines a spherical boundary, with its exact shape defined by the order p. We claim that this makes it possible to obtain arbitrarily shaped, curved boundaries more efficiently by combining just a few Lp units of different orders. We empirically evaluate the proposed Lp units on a number of datasets and show that MLPs consisting of the Lp units achieves the state-of-the-art results on a number of benchmark datasets. Hope to see many of you there, Razvan

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Talk by Hugo Tuesday 19th in Z300 at 10:30
by Razvan Pascanu 18 Nov '13

18 Nov '13

Hi all, This Tuesday, 19th November, Hugo Larochelle is visiting our lab and is going to give a talk. The time is 10:30 AM, place is Z300, pavilion McNicoll. Title: Deep Learning for Distribution Estimation Abstract:: In this talk, I will present my recent research on using neural networks for the task of distribution/density estimation, a fundamental problem in machine learning. Specifically, I will discuss the neural autoregressive distribution estimator (NADE), a state-of-the-art estimator of the probability distribution of data along with its various extensions. I will also describe a recent deep version of NADE, which again illustrates the statistical modelling power of deep models. Hope to see many of you there, Best, Razvan

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Tea talk this week Friday 3PM. room AA 3195
by Razvan Pascanu 14 Nov '13

14 Nov '13

The room has been confirmed. Is AA 3195. Best, Razvan On Wed, Nov 13, 2013 at 10:46 PM, Razvan Pascanu <r.pascanu(a)gmail.com>wrote: > > > Hi everyone, > > We've change the speaker for this tea talk. Hal is going to give a talk > instead. The room remains TBD, but same time 15:00 Friday. > > Hope to see many of you there. > Thanks. > > > title: A picture is worth 13.6 words (on average) > > abstract: I'll discuss some recent work I've been involved in that ties > language processing (which I know something about) and computer vision > (which I do not). Technically I'll focus on problems related to caption > generation. I'll highlight some of the challenges, discuss some successes > (and more failures) and discuss what I think the interesting problems are > moving forward. This talk is very informal, and feedback his highly > appreciated! This will include ideas and results that evolved from joint > work with Yiannis Aloimonos, Alex Berg, Tamara Berg, Jesse Dodge, Aleks > Ecins, Cornelia Fermuller, Amit Goyal, Xufeng Han, Alyssa Mensch, Margaret > Mitchell, Karl Stratos, Ching Lik Teo, Kota Yamaguchi and Yezhou Yang. > > >

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Tea talk this week Friday 3PM. room TBD [note change of speaker]
by Razvan Pascanu 13 Nov '13

13 Nov '13

Hi everyone, We've change the speaker for this tea talk. Hal is going to give a talk instead. The room remains TBD, but same time 15:00 Friday. Hope to see many of you there. Thanks. title: A picture is worth 13.6 words (on average) abstract: I'll discuss some recent work I've been involved in that ties language processing (which I know something about) and computer vision (which I do not). Technically I'll focus on problems related to caption generation. I'll highlight some of the challenges, discuss some successes (and more failures) and discuss what I think the interesting problems are moving forward. This talk is very informal, and feedback his highly appreciated! This will include ideas and results that evolved from joint work with Yiannis Aloimonos, Alex Berg, Tamara Berg, Jesse Dodge, Aleks Ecins, Cornelia Fermuller, Amit Goyal, Xufeng Han, Alyssa Mensch, Margaret Mitchell, Karl Stratos, Ching Lik Teo, Kota Yamaguchi and Yezhou Yang. On Wed, Nov 13, 2013 at 9:18 AM, Razvan Pascanu <r.pascanu(a)gmail.com> wrote: > Hi all, > > This week we will have a tea talk where Caglar, Kyunghyun and myself will > present the work submitted at AISTATS : Lp units for MLPs. > > The talk will try to go beyond what with submitted. We will try to > summarize some ideas we have to extend this work, namely my allowing more > flexibility in the units. I will also talk about how one can visualize what > these units are doing and how that could be useful. > > The abstract of the paper: > > In this paper we proposed a novel nonlinear unit, which is called as Lp > unit, for a multi-layer perceptron (MLP). The proposed Lp unit receives > signal from several projections of the layer below and computes the > normalized Lp norm. We notice two interesting interpretations of the Lp > unit. First, we note that the proposed unit is a generalization of a number > of conventional pooling operators such as average, root-mean-square and max > pooling widely used in, for instance, convolutional neural networks(CNN), > HMAX models and neocognitrons. Furthermore, under certain constraints, the > Lp unit is a generalization of the recently proposed maxout unit > (Goodfellow et al, 2013) which achieved the state-of-the-art object > recognition results on a number of benchmark datasets. Second, we provide a > geometrical interpretation of the activation function. Each Lp unit defines > a spherical boundary, with its exact shape defined by the order p. We claim > that this makes it possible to obtain arbitrarily shaped, curved boundaries > more efficiently by combining just a few Lp units of different orders. We > empirically evaluate the proposed Lp units on a number of datasets and show > that MLPs consisting of the Lp units achieves the state-of-the-art results > on a number of benchmark datasets. > > > Hope to see many of you there, > > Razvan >

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Tea talk this Week Friday 3PM in LISA room
by Razvan Pascanu 13 Nov '13

13 Nov '13

Hi all, This week we will have a tea talk where Caglar, Kyunghyun and myself will present the work submitted at AISTATS : Lp units for MLPs. The talk will try to go beyond what with submitted. We will try to summarize some ideas we have to extend this work, namely my allowing more flexibility in the units. I will also talk about how one can visualize what these units are doing and how that could be useful. The abstract of the paper: In this paper we proposed a novel nonlinear unit, which is called as Lp unit, for a multi-layer perceptron (MLP). The proposed Lp unit receives signal from several projections of the layer below and computes the normalized Lp norm. We notice two interesting interpretations of the Lp unit. First, we note that the proposed unit is a generalization of a number of conventional pooling operators such as average, root-mean-square and max pooling widely used in, for instance, convolutional neural networks(CNN), HMAX models and neocognitrons. Furthermore, under certain constraints, the Lp unit is a generalization of the recently proposed maxout unit (Goodfellow et al, 2013) which achieved the state-of-the-art object recognition results on a number of benchmark datasets. Second, we provide a geometrical interpretation of the activation function. Each Lp unit defines a spherical boundary, with its exact shape defined by the order p. We claim that this makes it possible to obtain arbitrarily shaped, curved boundaries more efficiently by combining just a few Lp units of different orders. We empirically evaluate the proposed Lp units on a number of datasets and show that MLPs consisting of the Lp units achieves the state-of-the-art results on a number of benchmark datasets. Hope to see many of you there, Razvan

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