40 citations · 72 across the 18 of their papers we have counts for
4 papers · 1 filter
A Margin-based Multiclass Generalization Bound via Geometric Complexity
Michael Munn, Benoit Dherin, Javier Gonzalvo
There has been considerable effort to better understand the generalization capabilities of deep neural networks both as a means to unlock a theoretical understanding of their succe…
Corridor Geometry in Gradient-Based Optimization
Benoit Dherin, Mihaela Rosca
We characterize regions of a loss surface as corridors when the continuous curves of steepest descent -- the solutions of the gradient flow -- become straight lines. We show that c…
On a continuous time model of gradient descent dynamics and instability in deep learning
Mihaela Rosca, Yan Wu, Chongli Qin +1
The recipe behind the success of deep learning has been the combination of neural networks and gradient-based optimization. Understanding the behavior of gradient descent however,…
Discretization Drift in Two-Player Games
Mihaela Rosca, Yan Wu, Benoit Dherin +1
Gradient-based methods for two-player games produce rich dynamics that can solve challenging problems, yet can be difficult to stabilize and understand. Part of this complexity ori…