Edouard Couplet

According to our database1, Edouard Couplet authored at least 11 papers between 2023 and 2026.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book  In proceedings  Article  PhD thesis  Dataset  Other 

Links

On csauthors.net:

Bibliography

2026
Improving on early exaggeration in t -SNE: Early hierarchization better preserves global structure.
Neurocomputing, 2026

Multi-Scale Stochastic Neighbor Embedding with Twice Adaptive Bandwidths.
Proceedings of the 34th European Symposium on Artificial Neural Networks, 2026

Interpretable Parametric Neighbour Embedding.
Proceedings of the 34th European Symposium on Artificial Neural Networks, 2026

2025
FUnc-SNE: A flexible, Fast, and Unconstrained algorithm for neighbour embeddings.
CoRR, September, 2025

Can MDS rival with t-SNE by using the symmetric Kullback-Leibler divergence\\ across neighborhoods as a pseudo-distance?
Proceedings of the 33rd European Symposium on Artificial Neural Networks, 2025

2024
Investigating latent representations and generalization in deep neural networks for tabular data.
Neurocomputing, 2024

Estimated neighbour sets and smoothed sampled global interactions are sufficient for a fast approximate tSNE.
Proceedings of the 32nd European Symposium on Artificial Neural Networks, 2024

Forget early exaggeration in t-SNE: early hierarchization preserves global structure.
Proceedings of the 32nd European Symposium on Artificial Neural Networks, 2024

2023
Natively Interpretable t-SNE.
Proceedings of the Machine Learning and Principles and Practice of Knowledge Discovery in Databases, 2023

Nesterov momentum and gradient normalization to improve t-SNE convergence and neighborhood preservation, without early exaggeration.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023

On the number of latent representations in deep neural networks for tabular data.
Proceedings of the 31st European Symposium on Artificial Neural Networks, 2023


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