136 citations · 217 across the 9 of their papers we have counts for
7 papers · 1 filter
Soft Attention: Does it Actually Help to Learn Social Interactions in Pedestrian Trajectory Prediction?
Laurent Boucaud, Daniel Aloise, Nicolas Saunier
We consider the problem of predicting the future path of a pedestrian using its motion history and the motion history of the surrounding pedestrians, called social information. Sin…
Exploring dual information in distance metric learning for clustering
Rodrigo Randel, Daniel Aloise, Alain Hertz
Distance metric learning algorithms aim to appropriately measure similarities and distances between data points. In the context of clustering, metric learning is typically applied…
Heuristics for optimizing 3D mapping missions over swarm-powered ad hoc clouds
Leandro R. Costa, Daniel Aloise, Luca G. Gianoli +1
Drones have been getting more and more popular in many economy sectors. Both scientific and industrial communities aim at making the impact of drones even more disruptive by empowe…
On Improving Deep Learning Trace Analysis with System Call Arguments
Quentin Fournier, Daniel Aloise, Seyed Vahid Azhari +1
Kernel traces are sequences of low-level events comprising a name and multiple arguments, including a timestamp, a process id, and a return value, depending on the event. Their ana…
Automatic Cause Detection of Performance Problems in Web Applications
Quentin Fournier, Naser Ezzati-Jivan, Daniel Aloise +1
The execution of similar units can be compared by their internal behaviors to determine the causes of their potential performance issues. For instance, by examining the internal be…
Empirical comparison between autoencoders and traditional dimensionality reduction methods
Quentin Fournier, Daniel Aloise
In order to process efficiently ever-higher dimensional data such as images, sentences, or audio recordings, one needs to find a proper way to reduce the dimensionality of such dat…
A Practical Survey on Faster and Lighter Transformers
Quentin Fournier, Gaétan Marceau Caron, Daniel Aloise
Recurrent neural networks are effective models to process sequences. However, they are unable to learn long-term dependencies because of their inherent sequential nature. As a solu…