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Related publications (147)

Stochastic Spectral Descent for Discrete Graphical Models

Volkan Cevher, Ya-Ping Hsieh, Edo Collins

Interest in deep probabilistic graphical models has increased in recent years, due to their state-of-the-art perfor- mance on many machine learning applications. Such models are typically trained with the stochastic gradient method, which can take a signif ...

Ieee-Inst Electrical Electronics Engineers Inc2016

Learning Structured Models for Segmentation of 2D and 3D Imagery

Pascal Fua, Graham Knott, Kevin Smith, Aurélien Lucchi, Carlos Joaquin Becker, Pablo Marquez Neila, Yunpeng Li

Efficient and accurate segmentation of cellular structures in microscopic data is an essential task in medical imaging. Many state-of-the-art approaches to image segmentation use structured models whose parameters must be carefully chosen for optimal perfo ...

Institute of Electrical and Electronics Engineers2015

A Bayesian Approach for Pervasive Estimation of Breaststroke Velocity Using a Wearable IMU

Kamiar Aminian, Grégoire Millet, Farzin Dadashi

An ubiquitous assessment of swimming velocity (main metric of the performance) is essential for the coach to provide a tailored feedback to the trainee. We present a probabilistic framework for the data-driven estimation of the swimming velocity at every c ...

Elsevier Science Bv2015

When Others Impinge upon Your Privacy

Mathias Jacques Jean-Marc Humbert

Privacy is defined as the right to control, edit, manage, and delete information about oneself and decide when, how, and to what extent this information is communicated to others. Therefore, every person should ideally be empowered to manage and protect hi ...

EPFL2015

Sparse Probabilistic Models

Andrei Giurgiu

This thesis is concerned with a number of novel uses of spatial coupling, applied to a class of probabilistic graphical models. These models include error correcting codes, random constraint satisfaction problems (CSPs) and statistical physics models calle ...

EPFL2015

High Resolution Air Pollution Maps in Urban Environments Using Mobile Sensor Networks

Alcherio Martinoli, Ali Marjovi, Adrian Arfire

We propose three modeling methods using a mobile sensor network to generate high spatio-temporal resolution air pollution maps for urban environments. In our deployment in Lausanne (Switzerland), dedicated sensing nodes are anchored to the public buses and ...

IEEE2015

Measurement of B-0 -> (Ds-KS0)pi(+) and B+ -> Ds-K+K+ branching fractions

Olivier Schneider, Yiming Li, Mingkui Wang, Chao Wang, Tagir Aushev, Sun Hee Kim, Tara Nanut, Jun Yong Kim, Ji Hyun Kim, Donghyun Kim

We report a measurement of the B-0 and B+ meson decays to the (Ds-KS0)pi(+) and Ds-K+K+ final states, respectively, using 657 x 10(6)B (B) over bar pairs collected at the gamma(4S) resonance with the Belle detector at the KEKB asymmetric-energy e(+)e(-) co ...

Amer Physical Soc2015

EMPLOYMENT OF SUBSPACE GAUSSIAN MIXTURE MODELS IN SPEAKER RECOGNITION

Petr Motlicek, Subhadeep Dey

This paper presents Subspace Gaussian Mixture Model (SGMM) approach employed as a probabilistic generative model to estimate speaker vector representations to be subsequently used in the speaker verification task. SGMMs have already been shown to significa ...

2015

EMPLOYMENT OF SUBSPACE GAUSSIAN MIXTURE MODELS IN SPEAKER RECOGNITION

Petr Motlicek, Subhadeep Dey

Idiap2015

Targeted vaccination based on a wireless sensor system

Marcel Salathé

Vaccination is one of the most effective ways to protect people from being infected by infectious disease. However, it is often impractical to vaccinate all people in a community due to various resource constraints. Therefore, targeted vaccination, which v ...

IEEE2015

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