Université de Montréal

About: Université de Montréal is a education organization based out in Montreal, Quebec, Canada. It is known for research contribution in the topics: Population & Poison control. The organization has 45641 authors who have published 100476 publications receiving 4004007 citations. The organization is also known as: University of Montreal & UdeM.

Foreign Branding and Its Effects on Product Perceptions and Attitudes

Abstract: With three experiments, the authors examine the notion that foreign branding—the strategy of pronouncing or spelling a brand name in a foreign language—triggers cultural stereotypes and influences ...

A guide to vehicle routing heuristics

Abstract: Several of the most important classical and modern heuristics for the vehicle routing problem are summarized and compared using four criteria: accuracy, speed, simplicity and flexibility. Computational results are reported.

A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues

Abstract: Sequential data often possesses hierarchical structures with complex dependencies between sub-sequences, such as found between the utterances in a dialogue. To model these dependencies in a generative framework, we propose a neural network-based generative architecture, with stochastic latent variables that span a variable number of time steps. We apply the proposed model to the task of dialogue response generation and compare it with other recent neural-network architectures. We evaluate the model performance through a human evaluation study. The experiments demonstrate that our model improves upon recently proposed models and that the latent variables facilitate both the generation of meaningful, long and diverse responses and maintaining dialogue state.

Motion: the long and short of it.

Abstract: Several authors have proposed that motion is analyzed by two separate processes: short-range and long-range. We claim that the differences between short-range and long-range motion phenomena are a direct consequence of the stimuli used in the two paradigms and are not evidence for the existence of two qualitatively different motion processes. We propose that a single style of motion analysis, similar to the well known Reichardt and Marr-Ullman motion detectors, underlies all motion phenomena. Although there are different detectors of this type specialized for different visual attributes (namely first-order and second-order stimuli), they all share the same mode of operation. We review the studies of second-order motion stimuli to show that they share the basic phenomena observed for first-order stimuli. The similarity across stimulus types suggests, not parallel streams of motion extraction, one short-range and passive and the other long-range and intelligent, but a concatenation of a common mode of initial motion extraction followed by a general inference process.