1 citations · 1 across the 4 of their papers we have counts for
4 papers
Preventing RNN from Using Sequence Length as a Feature
Jean-Thomas Baillargeon, Hélène Cossette, Luc Lamontagne
Recurrent neural networks are deep learning topologies that can be trained to classify long documents. However, in our recent work, we found a critical problem with these cells: th…
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models
Jean-Thomas Baillargeon, Luc Lamontagne
Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different leng…
Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations
Jean-Thomas Baillargeon, Nicolas Garneau
This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations. We present our…
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
Christopher Blier-Wong, Jean-Thomas Baillargeon, Hélène Cossette +2
Insurance companies gather a growing variety of data for use in the insurance process, but most traditional ratemaking models are not designed to support them. In particular, many…