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We propose an online learning algorithm to tackle the problem of learning under limited computational resources in a teacher-student scenario, over multiple visual cues. For each separate cue, we train an online learning algorithm that sacrifices performan ...
Due to indirect branch instructions, analyses on executables commonly suffer from the problem that a complete control flow graph of the program is not available. Data flow analysis has been proposed before to statically determine branch targets in many cas ...
Uncertainty Feature Optimization is a framework to cope with optimization problems due to noisy data, using an implicit characterazation of the noise. The Aircraft Scheduling Problem (ASP) is a particular case of such problems, where disruptions randomly p ...
This work describes the development of a software framework allowing to simulate range data acquired with different kind of sensors such as stereo camera and ultrasonic probe. The sensors characteristic error are modeled with probability density functions. ...
We present a probabilistic architecture for solving generically the problem of extracting the task constraints through a Programming by Demonstration (PbD) framework and for generalizing the acquired knowledge to various situations. We propose an approach ...
This paper introduces the 3SP Design Space Exploration System. 3SP automatically quantifies acceleration opportunities for programs across a wide range of heterogeneous architectures to allow designers to identify promising implementation platforms before ...
The following paper provides developers, designers and researchers of location-aware applications with a descriptive framework of applications that convey Mutual Location-Awareness. These applications rely on ubiquitous computing systems to inform people o ...
In this article, we describe a novel hardware-software design framework for prototyping cellular architectures in hardware. Based on an extensible platform of about 200 FPGAs, configured as a networked structure of processors, the hardware part of this com ...
Unexpected stimuli are a challenge to any machine learning algorithm. Here we identify distinct types of unexpected events, focusing on ’incongruent events’ - when ’general level’ and ’specific level’ classifiers give conflicting predictions. We define a f ...