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Preventing RNN from Using Sequence Length as a Feature
Recurrent neural networks are deep learning topologies that can be trained to classify long documents.
Assessing the Impact of Sequence Length Learning on Classification Tasks for Transformer Encoder Models
Classification algorithms using Transformer architectures can be affected by the sequence length learning problem whenever observations from different classes have a different length distribution.
Beer2Vec : Extracting Flavors from Reviews for Thirst-Quenching Recommandations
This paper introduces the Beer2Vec model that allows the most popular alcoholic beverage in the world to be encoded into vectors enabling flavorful recommendations.
Rethinking Representations in P&C Actuarial Science with Deep Neural Networks
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.
Representation Learning
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