6 citations · 13 across the 4 of their papers we have counts for
4 papers · 1 filter
Heterogeneous Ensemble Knowledge Transfer for Training Large Models in Federated Learning
Yae Jee Cho, Andre Manoel, Gauri Joshi +2
Federated learning (FL) enables edge-devices to collaboratively learn a model without disclosing their private data to a central aggregating server. Most existing FL algorithms req…
Federated Survival Analysis with Discrete-Time Cox Models
Mathieu Andreux, Andre Manoel, Romuald Menuet +2
Building machine learning models from decentralized datasets located in different centers with federated learning (FL) is a promising approach to circumvent local data scarcity whi…
Efficient Per-Example Gradient Computations in Convolutional Neural Networks
Gaspar Rochette, Andre Manoel, Eric W. Tramel
Deep learning frameworks leverage GPUs to perform massively-parallel computations over batches of many training examples efficiently. However, for certain tasks, one may be interes…
Entropy and mutual information in models of deep neural networks
Marylou Gabrié, Andre Manoel, Clément Luneau +4
We examine a class of deep learning models with a tractable method to compute information-theoretic quantities. Our contributions are three-fold: (i) We show how entropies and mutu…