2 citations · 3 across the 4 of their papers we have counts for
4 papers
Emergence of Fibrations, Compression, and Symmetry Breaking in Artificial Neural Networks
Osvaldo M Velarde, Lucas C Parra, Alireza Hashemi +1
Artificial neural networks are often regarded as powerful yet opaque black boxes. Here, we demonstrate that learning in deep neural networks generates local symmetries known in gra…
Recurrent Joint Embedding Predictive Architecture with Recurrent Forward Propagation Learning
Osvaldo M Velarde, Lucas C Parra
Conventional computer vision models rely on very deep, feedforward networks processing whole images and trained offline with extensive labeled data. In contrast, biological vision…
The Role of Fibration Symmetries in Geometric Deep Learning
Osvaldo Velarde, Lucas Parra, Paolo Boldi +1
Geometric Deep Learning (GDL) unifies a broad class of machine learning techniques from the perspectives of symmetries, offering a framework for introducing problem-specific induct…
Independent evaluation of state-of-the-art deep networks for mammography
Osvaldo Matias Velarde, Lucas Parra
Deep neural models have shown remarkable performance in image recognition tasks, whenever large datasets of labeled images are available. The largest datasets in radiology are avai…