output
20232026
researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions

4 papers

eess.IV2026

Discovering shared interpretable operations in image compression autoencoders

Caroline Mazini Rodrigues, Nicolas Keriven, Thomas Maugey

With the increasing adoption of deep learning for applications such as image compression, improvements in the rate-distortion trade-off have been achieved at the cost of increasing…

cs.LG2025

Backward Oversmoothing: why is it hard to train deep Graph Neural Networks?

Nicolas Keriven

Oversmoothing has long been identified as a major limitation of Graph Neural Networks (GNNs): input node features are smoothed at each layer and converge to a non-informative repre…

stat.ML2024

Node Regression on Latent Position Random Graphs via Local Averaging

Martin Gjorgjevski, Nicolas Keriven, Simon Barthelmé +1

Node regression consists in predicting the value of a graph label at a node, given observations at the other nodes. To gain some insight into the performance of various estimators…

cs.CV2023

Learning Kernel-Modulated Neural Representation for Efficient Light Field Compression

Jinglei Shi, Yihong Xu, Christine Guillemot

Light field is a type of image data that captures the 3D scene information by recording light rays emitted from a scene at various orientations. It offers a more immersive percepti…