Novembre 2016 - Lisa_teatalk - mailman.iro.umontreal.ca

[TeaTalk & LURG] Nov. 28th, Harm de Vries on "GuessWhat?! Visual object discovery through multi-modal dialogue"
by Junyoung Chung 28 Nov '16

28 Nov '16

Hi everyone, we don't have a tea talk on this Friday (Nov 25), but instead we will have one on next Monday (Nov 28). This talk is also going to be the LURG meeting of the week. Harm will present his recent work on "GuessWhat?! Visual object discovery through multi-modal dialogue". Who: Harm de Vries When: Nov. 28th (Monday) 3PM Where: PAA3195 Title: GuessWhat?! Visual object discovery through multi-modal dialogue Abstract: We introduce GuessWhat?!, a two-player guessing game as a testbed for research on the interplay of computer vision and dialogue systems. The goal of the game is to locate an unknown object in a rich image scene by asking a sequence of questions. Higher-level image understanding, like spatial reasoning and language grounding, is required to solve the proposed task. Our key contribution is the collection of a large-scale dataset consisting of 150K human-played games with a total of 800K visual question-answer pairs on 66K images. We explain our design decisions in collecting the dataset and introduce the oracle and questioner tasks that are associated with the two players of the game. We prototyped deep learning models to establish initial baselines of the introduced tasks. Best, --Junyoung

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No tea talk this week!
by Junyoung Chung 24 Nov '16

24 Nov '16

Hi everyone, as mentioned in the previous email, there will be no tea talk on Friday. We will have one on Nov 28. Best, --Junyoung

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[Talk] Prof. Wulfram Gerstner on "Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience"
by Junyoung Chung 24 Nov '16

24 Nov '16

Hi everyone, on next Tuesday, we will have a guest tutorial on neuroscience by professor Wulfram Gerstner who is visiting our lab for two weeks. Looking forward to see many of you there! Who: Wulfram Gerstner When: Nov. 22 (Tuesday) 3PM-5PM Where: AA6214 Title: Synaptic Plasticity to Learning Rules: an approach of theoretical neuroscience Abstract: In this tutorial I will show what type of constraints experimental data set for biologically plausible learning rules and why inside these constraints we (as theoretical neuroscientists) have a lot of freedom to think about learning rules that are functionally relevant in learning paradigms. Taking examples from unsupervised learning, reinforcement learning, and surprise modulated learning I will point out potential links (and differences) to machine learning approaches. Bio: Wulfram Gerstner is Director of the Laboratory of Computational Neuroscience LCN at the EPFL. He studied physics at the universities of Tubingen and Munich and received a PhD from the Technical University of Munich. His research in computational neuroscience concentrates on models of spiking neurons and spike-timing dependent plasticity, on the problem of neuronal coding in single neurons and populations, as well as on the role of spatial representation for navigation of rat-like autonomous agents. He currently has a joint appointment at the School of Life Sciences and the School of Computer and Communications Sciences at the EPFL. He teaches courses for Physicists, Computer Scientists, Mathematicians, and Life Scientists. Best, --Junyoung

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Dialogue-based learning talk by Jason Weston
by Tegan Maharaj 17 Nov '16

17 Nov '16

Hi everyone, The tea talk this week will be Jason Weston's talk about dialogue. Who: Jason Weston When: Nov. 17 (Today) 3:00PM Where: PAA 1207 Title: Dialog-based Language Learning Abstract: We study how to learn to use language via dialog-based language learning, where supervision is given naturally and implicitly in the response of the dialog partner during the conversation. Experiments are in two domains: the bAbI dataset of (Weston et al., 2015) and the larger movieQA dataset from (Dodge et al., 2015). We evaluate a set of baseline learning strategies on these tasks, and show that a novel model incorporating predictive lookahead is a promising approach for learning from a teacher's response. In particular, a surprising result is that it can learn to answer questions correctly without any reward-based supervision at all. This is joint work with Jiwei Li, Alex Miller, Sumit Chopra and Marc'Aurelio Ranzato Hope to see you there! Cheers, Tegan

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[TeaTalk] Talk by Laurent Dinh: Density estimation using Real NVP
by Junyoung Chung 11 Nov '16

11 Nov '16

Hi everyone, on this Friday, Laurent will talk about his work "Density estimation using Real NVP". This work will be presented as an oral talk at NIPS Deep Learning Symposium. Let's give him some helpful feedbacks. Who: Laurent Dinh When: Nov. 11th (Friday) 11AM Where: AA6214 Title: Density estimation using Real NVP Abstract: Unsupervised learning of probabilistic models is a central yet challenging problem in machine learning. Specifically, designing models with tractable learning, sampling, inference and evaluation is crucial in solving this task. We extend the space of such models using real-valued non-volume preserving (real NVP) transformations, a set of powerful invertible and learnable transformations, resulting in an unsupervised learning algorithm with exact log-likelihood computation, exact sampling, exact inference of latent variables, and an interpretable latent space. We demonstrate its ability to model natural images on four datasets through sampling, log-likelihood evaluation and latent variable manipulations. Cheers, --Junyoung

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No teatalk this week!
by Junyoung Chung 02 Nov '16

02 Nov '16

Hi everyone, there will be no teatalk this week due to ICLR deadline! Best, --Junyoung

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