On the number of latent representations in deep neural networks for tabular data. - Edouard Couplet; Pierre Lambert; Michel Verleysen; John A. Lee 001; Cyril de Bodt (ESANN, 2023)

Nesterov momentum and gradient normalization to improve t-SNE convergence and neighborhood preservation, without early exaggeration. - Pierre Lambert; John A. Lee 001; Edouard Couplet; Cyril de Bodt (ESANN, 2023)

Natively Interpretable t-SNE. - Edouard Couplet; Pierre Lambert; Michel Verleysen; Dounia Mulders; John A. Lee 001; Cyril de Bodt (PKDD/ECML Workshops (1), 2023)

Forget early exaggeration in t-SNE: early hierarchization preserves global structure. - John A. Lee 001; Edouard Couplet; Pierre Lambert; Ludovic Journaux; Dounia Mulders; Cyril de Bodt; Michel Verleysen (ESANN, 2024)

Estimated neighbour sets and smoothed sampled global interactions are sufficient for a fast approximate tSNE. - Pierre Lambert; Edouard Couplet; Cyril de Bodt; John A. Lee 001 (ESANN, 2024)

Investigating latent representations and generalization in deep neural networks for tabular data. - Edouard Couplet; Pierre Lambert; Michel Verleysen; John A. Lee 001; Cyril de Bodt (Neurocomputing, 2024)