Learning and Predicting Multimodal Daily Life Patterns from Cell Phones

Katayoun Farrahi, Daniel Gatica-Perez
2009
Article de conférence

In this paper, we investigate the multimodal nature of cell phone data in terms of discovering recurrent and rich patterns in people's lives. We present a method that can discover routines from multiple modalities (location and proximity) jointly modeled, and that uses these informative routines to predict unlabeled or missing data. Using a joint representation of location and proximity data over approximately 10 months of 97 individuals' lives, Latent Dirichlet Allocation is applied for the unsupervised learning of topics describing people's most common locations jointly with the most common types of interactions at these locations. We further successfully predict where and with how many other individuals users will be, for people with both highly and lowly varying lifestyles.

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Katayoun Farrahi, Daniel Gatica-Perez
2009
Article de conférence

Résumé

Official source

https://infoscience.epfl.ch/record/145979?ln=fr

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Measuring the Impact of Model and Input Heterogeneity in Personalized Federated Learning

Freya Behrens

Federated Learning is explicitly designed for learning a global model from distributed, possibly sensitive non-i.i.d. data at different clients. In reality, this scenario is not always legible since in some cases the heterogeneous needs of clients cannot be packaged into a single global model, e.g. in next-word prediction on mobile phones where different clients might express themselves differently. Thus, in personalized federated learning the goal is to learn a personal model for each client individually by training a global model to be easy to adapt locally. Such a formulation is also known in meta-learning, where the goal is to learn a global model that can be easily adapted to different tasks rather than clients. In this project, we examine the differences and similarities between algorithms from the two fields. We furthermore develop a benchmarking dataset that captures different aspects of heterogeneity between the clients. On this we evaluate both when personalization in federated learning is actually beneficial over individual training and compare which algorithms from meta-learning and personalized federated learning fare best.

2021

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Téléphone mobile

Un téléphone mobile, téléphone portable ou téléphone cellulaire est un appareil électronique de télécommunication, normalement portatif, offrant une fonction de téléphonie mobile et pouvant être uti

Allocation de Dirichlet latente

Dans le domaine du traitement automatique des langues, l’allocation de Dirichlet latente (de l’anglais Latent Dirichlet Allocation) ou LDA est un modèle génératif probabiliste permettant d’expliquer

Apprentissage non supervisé

Dans le domaine informatique et de l'intelligence artificielle, l'apprentissage non supervisé désigne la situation d'apprentissage automatique où les données ne sont pas étiquetées (par exemple étiq