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Probabilistic graphical models for human interaction analysis

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Enhancing Session-Based Recommendations through Sequential Modeling

Boi Faltings, Vincent Jean Fabrice Schickel, Stéphane Bernard Martin

Recommender systems typically determine the items they should recommend by learning models of user-preferences. Most often, those preferences are modeled as static and independent of context. In real life however, users consider items in sequence: TV serie ...

ACM2018

GestureGAN for Hand Gesture-to-Gesture Translation in the Wild

Yan Yan, Wei Wang

Hand gesture-to-gesture translation in the wild is a challenging task since hand gestures can have arbitrary poses, sizes, locations and self-occlusions. Therefore, this task requires a high-level understanding of the mapping between the input source gestu ...

ASSOC COMPUTING MACHINERY2018

We develop approximate inference and learning methods for facilitating the use of probabilistic modeling techniques motivated by applications in two different areas. First, we consider the ill-posed inverse problem of recovering an image from an underdeter ...

EPFL2017

Automatic speech recognition (ASR) is a fascinating area of research towards realizing humanmachine interactions. After more than 30 years of exploitation of Gaussian Mixture Models (GMMs), state-of-the-art systems currently rely on Deep Neural Network (DN ...

Idiap2016

Reconstructing Curvilinear Networks using Path Classifiers and Integer Programming

Pascal Fua, Engin Türetken, Fethallah Benmansour, Przemyslaw Rafal Glowacki

We propose a novel Bayesian approach to automated delineation of curvilinear structures that form complex and potentially loopy networks. By representing the image data as a graph of potential paths, we first show how to weight these paths using discrimina ...

Institute of Electrical and Electronics Engineers2016

Adaptive regularized diffusion adaptation over multitask networks

Ali H. Sayed, Sadaf Monajemi

The focus of this paper is on multitask learning over adaptive networks where different clusters of nodes have different objectives. We propose an adaptive regularized diffusion strategy using Gaussian kernel regularization to enable the agents to learn ab ...

2015

Learning Discriminative Features and Structured Models for Segmentation in Microscopy and Natural Images

Aurélien Lucchi

Segmenting images is a significant challenge that has drawn a lot of attention from different fields of artificial intelligence and has many practical applications. One such challenge addressed in this thesis is the segmentation of electron microscope (EM) ...

EPFL2013

Consider a two-class classification problem where the number of features is much larger than the sample size. The features are masked by Gaussian noise with mean zero and covariance matrix Sigma, where the precision matrix Omega = Sigma(-1) is unknown but ...

Institute of Mathematical Statistics2013

Discriminative modeling of spectro-temporal evolution of covert visuospatial attention for BCI

José del Rocio Millán Ruiz, Robert Leeb, Luca Tonin, Gangadhar Garipelli

Due to its intuitive nature, the decoding of covert visuospatial attention has recently been proposed to be useful for Brain–Computer Interface (BCI) applications. In order to identify neural correlates of covert spatial visual attention, state of the art ...

2012

Roles are a key aspect of social interactions, as they contribute to the overall predictability of social behavior (a necessary requirement to deal effectively with the people around us), and they result in stable, possibly machine-detectable behavioral pa ...

2012