[seminarios-mlpb] Re: [seminarios-mlpb] Re: [seminarios-mlpb] Tuesday, December 14th, 13:00, Sala PA2: Mário Figueiredo on "Network Inference from Co-occurences"
- From: Dario WurmD <wurmdario@xxxxxxxxx>
- To: seminarios-mlpb@xxxxxxxxxxxxx
- Date: Mon, 13 Dec 2010 21:13:08 +0000
é só apareceres rapaz
On Mon, Dec 13, 2010 at 19:10, Paulo Gomes <pauloxgomes@xxxxxxxxx> wrote:
> Olá,
> sou bolseiro do INESC-ID e gostaria de assistir à apresentação.
> Saudações Académicas
> Paulo Gomes
>
> 2010/12/7 Andre Martins <afm+@xxxxxxxxxx <afm%2B@xxxxxxxxxx>>
>
> TALK ANNOUNCEMENT:
>> ***********************************************************************
>>
>> Priberam Machine Learning Lunch Seminar
>> Speaker: Mário Figueiredo (IT)
>> Venue: IST Alameda, Sala PA2 (Edifício de Pós-Graduação)
>> Date: Tuesday, December 14th, 2010
>> Time: 13:00
>> Lunch will be provided
>>
>> Title: Network Inference from Co-occurences
>>
>> Abstract:
>>
>> Inferring network structures is a central problem arising in numerous
>> fields of science and technology, including communication systems, biology,
>> sociology, and neuroscience. Unfortunately, it is often difficult, or
>> impossible, to obtain data that directly reveal the underlying network
>> structure, and so one must infer a network from incomplete data. In this
>> talk, we will look at the problem of inferring network structure from
>> ``co-occurrence" observations.
>> These observations identify which network components (e.g., switches and
>> routers, in a communications network, or genes, in a gene regulatory
>> network) co-occur in a path, but do not indicate the order in which they
>> occur in that path. Without order information, the number of networks that
>> are consistent with the data grows exponentially with the size of the
>> network (i.e., the number of nodes). Yet, the basic engineering/evolutionary
>> principles underlying most
>> networks strongly suggest that not all networks consistent with the
>> observations are equally likely. In particular, nodes that often co-occur
>> are probably closer than nodes that rarely co-occur. This rationale suggests
>> modeling co-occurrence observations as independent realizations of a
>> Markovian random walk on the network, subjected to a random permutation to
>> account for the lack of order information. Treating permutations as missing
>> data, allows deriving
>> an expectation-–maximization (EM) algorithm for estimating the random walk
>> parameters. The model and EM algorithm significantly simplify the problem,
>> but the computational complexity of the reconstruction process does grow
>> exponentially in the length of each transmission path. For networks with
>> long paths, the exact E-step may be computationally intractable. We thus
>> propose a polynomial-time Monte Carlo EM algorithm based on importance
>> sampling and derive conditions that
>> ensure convergence of the algorithm with high probability. Finally, we
>> report simulations and experiments with Internet measurements and
>> inference of biological networks that demonstrate the promise of this
>> approach.
>>
>> The work reported in this talk was done in collaboration with Prof.
>> Michael Rabbat (McGill University, Canada) and Prof. Robert D. Nowak
>> (University of Wisconsin, USA).
>>
>> --
>>
>> Bio: Mário A. T. Figueiredo received EE, MSc, PhD, and "Agregado"
>> degrees in electrical and computer engineering, all from Instituto
>> Superior Técnico (IST), Technical University of Lisbon (TULisbon), Portugal,
>> in 1985, 1990, 1994, and 2004, respectively. Since 1994, he has been with
>> the faculty of the Department of Electrical and Computer
>> Engineering, IST. He is also area and group coordinator at Instituto de
>> Telecomunicações, a private not-for-profit research institution. Mário
>> Figueiredo spent sabbatical leaves at the Department of Computer Science
>> and Engineering, Michigan State University, and the Department
>> of Electrical and Computer Engineering, University of Wisconsin-Madison,
>> in 1998 and 2005, respectively. He is a Fellow of the IEEE and of the IAPR
>> (International Association for Pattern Recognition) and a member of the
>> Image, Video, and Multidimensional Signal Processing Technical Committee of
>> the IEEE.
>>
>> Mário Figueiredo's scientific interests include image processing and
>> analysis, pattern recognition, statistical learning, and optimization. He
>> received the 1995 Portuguese IBM Scientific Prize and the 2008
>> UTL/Santander-Totta Scientific Prize. He is/was associate editor of the
>> following journals: IEEE Transactions on Image Processing, IEEE Transactions
>> on Pattern Analysis and Machine Intelligence (IEEE-TPAMI), IEEE Transactions
>> on Mobile Computing, Pattern Recognition Letters, and Signal Processing. He
>> is/was guest co-editor of special issues of the IEEE-TPAMI, the IEEE
>> Transactions on Signal Processing, and the IEEE Journal of Selected Topics
>> in Signal Processing. Mário Figueiredo was a co-chair of the 2001 and 2003
>> Workshops on Energy Minimization Methods in Computer Vision and Pattern
>> Recognition, and has served as a member of
>> organizing/program/technical/scientific committees of many
>> international conferences, including ICIP, ICASSP, CVPR, ICML,
>> ICPR, NIPS, IGARSS, MLSP, IJCNN, and others.
>>
>>
>>
>>
>>
>
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