Ricardo Araujo

AAAI Conference 2016 Conference Paper

On the Performance of GoogLeNet and AlexNet Applied to Sketches

• Pedro Ballester
• Ricardo Araujo

This work provides a study on how Convolutional Neural Networks, trained to identify objects primarily in photos, perform when applied to more abstract representations of the same objects. Our main goal is to better understand the generalization abilities of these networks and their learned inner representations. We show that both GoogLeNet and AlexNet networks are largely unable to recognize abstract sketches that are easily recognizable by humans. Moreover, we show that the measured efficacy vary considerably across different classes and we discuss possible reasons for this.

AAAI Conference 2015 Conference Paper

Person Identification Using Anthropometric and Gait Data from Kinect Sensor

• Virginia Andersson
• Ricardo Araujo

Uniquely identifying individuals using anthropometric and gait data allows for passive biometric systems, where cooperation from the subjects being identified is not required. In this paper, we report on experiments using a novel data set composed of 140 individuals walking in front of a Microsoft Kinect sensor. We provide a methodology to extract anthropometric and gait features from this data and show results of applying different machine learning algorithms on subject identification tasks. Focusing on KNN classifiers, we discuss how accuracy varies in different settings, including number of individuals in a gallery, types of attributes used and number of considered neighbors. Finally, we compare the obtained results with other results in the literature, showing that our approach has comparable accuracy for large galleries.

IJCAI Conference 2007 Conference Paper

• Ricardo Araujo
• Luis Lamb

Multiagent distributed resource allocation requires that agents act on limited, localized information with minimum communication overhead in order to optimize the distribution of available resources. When requirements and constraints are dynamic, learning agents may be needed to allow for adaptation. One way of accomplishing learning is to observe past outcomes, using such information to improve future decisions. When limits in agents' memory or observation capabilities are assumed, one must decide on how large should the observation window be. We investigate how this decision influences both agents' and system's performance in the context of a special class of distributed resource allocation problems, namely dispersion games. We show by numerical experiments over a specific dispersion game (the Minority Game) that in such scenario an agent's performance is non-monotonically correlated with her memory size when all other agents are kept unchanged. We then provide an information-theoretic explanation for the observed behaviors, showing that a downward causation effect takes place.