7 citations · 14 across the 7 of their papers we have counts for
16 papers
Deep Reinforcement Learning for L3 Slice Localization in Sarcopenia Assessment
Othmane Laousy, Guillaume Chassagnon, Edouard Oyallon +3
Sarcopenia is a medical condition characterized by a reduction in muscle mass and function. A quantitative diagnosis technique consists of localizing the CT slice passing through t…
Decoupled Greedy Learning of CNNs for Synchronous and Asynchronous Distributed Learning
Eugene Belilovsky, Louis Leconte, Lucas Caccia +2
A commonly cited inefficiency of neural network training using back-propagation is the update locking problem: each layer must wait for the signal to propagate through the full net…
Low-Rank Projections of GCNs Laplacian
Nathan Grinsztajn, Philippe Preux, Edouard Oyallon
In this work, we study the behavior of standard models for community detection under spectral manipulations. Through various ablation experiments, we evaluate the impact of bandpas…
Interferometric Graph Transform for Community Labeling
Nathan Grinsztajn, Louis Leconte, Philippe Preux +1
We present a new approach for learning unsupervised node representations in community graphs. We significantly extend the Interferometric Graph Transform (IGT) to community labelin…
The Unreasonable Effectiveness of Patches in Deep Convolutional Kernels Methods
Louis Thiry, Michael Arbel, Eugene Belilovsky +1
A recent line of work showed that various forms of convolutional kernel methods can be competitive with standard supervised deep convolutional networks on datasets like CIFAR-10, o…
Interferometric Graph Transform: a Deep Unsupervised Graph Representation
Edouard Oyallon
We propose the Interferometric Graph Transform (IGT), which is a new class of deep unsupervised graph convolutional neural network for building graph representations. Our first con…