10 citations · 10 across the 4 of their papers we have counts for
5 papers
Batch Normalization for Neural Networks on Complex Domains
Xuan Son Nguyen, Nistor Grozavu
Riemannian neural networks have proven effective in solving a variety of machine learning tasks. The key to their success lies in the development of principled Riemannian analogs o…
Neural Networks on Symmetric Spaces of Noncompact Type
Xuan Son Nguyen, Shuo Yang, Aymeric Histace
Recent works have demonstrated promising performances of neural networks on hyperbolic spaces and symmetric positive definite (SPD) manifolds. These spaces belong to a family of Ri…
Siegel Neural Networks
Xuan Son Nguyen, Aymeric Histace, Nistor Grozavu
Riemannian symmetric spaces (RSS) such as hyperbolic spaces and symmetric positive definite (SPD) manifolds have become popular spaces for representation learning. In this paper, w…
SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model
Loubna Ben Allal, Anton Lozhkov, Elie Bakouch +19
While large language models have facilitated breakthroughs in many applications of artificial intelligence, their inherent largeness makes them computationally expensive and challe…
Matrix Manifold Neural Networks++
Xuan Son Nguyen, Shuo Yang, Aymeric Histace
Deep neural networks (DNNs) on Riemannian manifolds have garnered increasing interest in various applied areas. For instance, DNNs on spherical and hyperbolic manifolds have been d…