14 citations · 39 across the 15 of their papers we have counts for
8 papers · 1 filter
MIND: Monge Inception Distance for Generative Models Evaluation
Quentin Berthet, Yu-Han Wu, Clement Crepy +3
We propose the Monge Inception Distance (MIND), a metric for evaluating generative models that addresses key limitations of the widely adopted Fréchet Inception Distance (FID). The…
Differentiable Knapsack and Top-k Operators via Dynamic Programming
Germain Vivier-Ardisson, Michaël E. Sander, Axel Parmentier +1
Knapsack and Top-k operators are useful for selecting discrete subsets of variables. However, their integration into neural networks is challenging as they are piecewise constant,…
Loss Functions and Operators Generated by f-Divergences
Vincent Roulet, Tianlin Liu, Nino Vieillard +2
The logistic loss (a.k.a. cross-entropy loss) is one of the most popular loss functions used for multiclass classification. It is also the loss function of choice for next-token pr…
Joint Learning of Energy-based Models and their Partition Function
Michael E. Sander, Vincent Roulet, Tianlin Liu +1
Energy-based models (EBMs) offer a flexible framework for parameterizing probability distributions using neural networks. However, learning EBMs by exact maximum likelihood estimat…
Fast, Differentiable and Sparse Top-k: a Convex Analysis Perspective
Michael E. Sander, Joan Puigcerver, Josip Djolonga +2
The top-k operator returns a sparse vector, where the non-zero values correspond to the k largest values of the input. Unfortunately, because it is a discontinuous function, it is…
Do Residual Neural Networks discretize Neural Ordinary Differential Equations?
Michael E. Sander, Pierre Ablin, Gabriel Peyré
Neural Ordinary Differential Equations (Neural ODEs) are the continuous analog of Residual Neural Networks (ResNets). We investigate whether the discrete dynamics defined by a ResN…
Sinkformers: Transformers with Doubly Stochastic Attention
Michael E. Sander, Pierre Ablin, Mathieu Blondel +1
Attention based models such as Transformers involve pairwise interactions between data points, modeled with a learnable attention matrix. Importantly, this attention matrix is norm…
Momentum Residual Neural Networks
Michael E. Sander, Pierre Ablin, Mathieu Blondel +1
The training of deep residual neural networks (ResNets) with backpropagation has a memory cost that increases linearly with respect to the depth of the network. A way to circumvent…