David Romero — accepted AI-conference papers

2022

Phonotactic Language Recognition Using A Universal Phoneme Recognizer and A Transformer Architecture

David Romero, Luis Fernando D'Haro, Marcos Estecha-Garitagoitia, Christian Salamea

ICASSP 2022accepted

In this paper, we describe a phonotactic language recognition model that effectively manages long and short n-gram input sequences to learn contextual phonotactic-based vector embeddings. Our approach uses a transformer-based encoder that integrates a sliding window attention to attempt finding disc…

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2020

Attentive Group Equivariant Convolutional Networks

David Romero, Erik Bekkers, Jakub Tomczak, Mark Hoogendoorn

ICML 2020poster

Although group convolutional networks are able to learn powerful representations based on symmetry patterns, they lack explicit means to learn meaningful relationships among them (e.g., relative positions and poses). In this paper, we present attentive group equivariant convolutions, a generalizatio…

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