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Robust machine learning for neuroscientific inference

Publications associées (168)

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Semi-supervised and unsupervised kernel-based novelty detection with application to remote sensing images

Frank Grégoire Jean de Morsier

The main challenge of new information technologies is to retrieve intelligible information from the large volume of digital data gathered every day. Among the variety of existing data sources, the satellites continuously observing the surface of the Earth ...

EPFL2014

Adaptation, Learning, and Optimization over Networks

Ali H. Sayed

This work deals with the topic of information processing over graphs. The presentation is largely self-contained and covers results that relate to the analysis and design of multi-agent networks for the distributed solution of optimization, adaptation, and ...

NOW Publishers2014

Scalable Collaborative Bayesian Preference Learning

Matthias Seeger, Mohammad Emtiyaz Khan, Young Jun Ko

Learning about users’ utilities from preference, discrete choice or implicit feedback data is of integral importance in e-commerce, targeted advertising and web search. Due to the sparsity and diffuse nature of data, Bayesian approaches hold much promise, ...

2014

Pre-Impact Fall Detection: Optimal Sensor Positioning Based on a Machine Learning Paradigm

Silvestro Micera, Fiorenzo Artoni

The aim of this study was to identify the best subset of body segments that provides for a rapid and reliable detection of the transition from steady walking to a slipping event. Fifteen healthy young subjects managed unexpected perturbations during walkin ...

Public Library Science2014

A Probabilistic Approach to Mining Mobile Phone Data Sequences

Daniel Gatica-Perez, Katayoun Farrahi

We present a new approach to address the problem of large sequence mining from big data. The particular problem of interest is the effective mining of long sequences from large-scale location data to be practical for Reality Mining applications, which suff ...

2014

Computational Education using Latent Structured Prediction

Computational education offers an important add-on to conventional teaching. To provide optimal learning conditions, accurate representation of students' current skills and adaptation to newly acquired knowledge are essential. To obtain sufficient represen ...

2014

Two-stage online inference model for traffic pattern analysis and anomaly detection

Kwang Moo Yi

In this paper, we propose a method for modeling trajectory patterns with both regional and velocity observations through the probabilistic topic model. By embedding Gaussian models into the discrete topic model framework, our method uses continuous velocit ...

Springer Verlag2014

Automatic Feature Learning for Spatio-Spectral Image Classification With Sparse SVM

Devis Tuia

Including spatial information is a key step for successful remote sensing image classification. In particular, when dealing with high spatial resolution, if local variability is strongly reduced by spatial filtering, the classification performance results ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Non-Linear Domain Adaptation with Boosting

Pascal Fua, Carlos Joaquin Becker, Christos Marios Christoudias

A common assumption in machine vision is that the training and test samples are drawn from the same distribution. However, there are many problems when this assumption is grossly violated, as in bio-medical applications where different acquisitions can gen ...

2013

Similarity Learning Over Large Collaborative Networks

Majid Yazdani

In this thesis, we propose novel solutions to similarity learning problems on collaborative networks. Similarity learning is essential for modeling and predicting the evolution of collaborative networks. In addition, similarity learning is used to perform ...

EPFL2013