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cs.LG2022
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…
cs.LG2022
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…