437 citations · 624 across the 4 of their papers we have counts for
9 papers · 1 filter
Variance Reduction in Deep Learning: More Momentum is All You Need
Lionel Tondji, Sergii Kashubin, Moustapha Cisse
Variance reduction (VR) techniques have contributed significantly to accelerating learning with massive datasets in the smooth and strongly convex setting (Schmidt et al., 2017; Jo…
Fairness with Overlapping Groups
Forest Yang, Moustapha Cisse, Sanmi Koyejo
In algorithmically fair prediction problems, a standard goal is to ensure the equality of fairness metrics across multiple overlapping groups simultaneously. We reconsider this sta…
On Mixup Regularization
Luigi Carratino, Moustapha Cissé, Rodolphe Jenatton +1
Mixup is a data augmentation technique that creates new examples as convex combinations of training points and labels. This simple technique has empirically shown to improve the ac…
Fooling End-to-end Speaker Verification by Adversarial Examples
Felix Kreuk, Yossi Adi, Moustapha Cisse +1
Automatic speaker verification systems are increasingly used as the primary means to authenticate costumers. Recently, it has been proposed to train speaker verification systems us…
Turning Your Weakness Into a Strength: Watermarking Deep Neural Networks by Backdooring
Yossi Adi, Carsten Baum, Moustapha Cisse +2
Deep Neural Networks have recently gained lots of success after enabling several breakthroughs in notoriously challenging problems. Training these networks is computationally expen…
Unbounded cache model for online language modeling with open vocabulary
Edouard Grave, Moustapha Cisse, Armand Joulin
Recently, continuous cache models were proposed as extensions to recurrent neural network language models, to adapt their predictions to local changes in the data distribution. The…