activity
20182021
most citedOn the Relationship between Self-Attention and Convolutional Layers

89 citations · 89 across the 2 of their papers we have counts for

collaborators

6 papers

cs.CV2021

Differentiable Patch Selection for Image Recognition

Jean-Baptiste Cordonnier, Aravindh Mahendran, Alexey Dosovitskiy +3

Neural Networks require large amounts of memory and compute to process high resolution images, even when only a small part of the image is actually informative for the task at hand…

cs.CV2020

Group Equivariant Stand-Alone Self-Attention For Vision

David W. Romero, Jean-Baptiste Cordonnier

We provide a general self-attention formulation to impose group equivariance to arbitrary symmetry groups. This is achieved by defining positional encodings that are invariant to t…

cs.CL2019

Robust Cross-lingual Embeddings from Parallel Sentences

Ali Sabet, Prakhar Gupta, Jean-Baptiste Cordonnier +2

Recent advances in cross-lingual word embeddings have primarily relied on mapping-based methods, which project pretrained word embeddings from different languages into a shared spa…

cs.LG201989 cited

On the Relationship between Self-Attention and Convolutional Layers

Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi

Recent trends of incorporating attention mechanisms in vision have led researchers to reconsider the supremacy of convolutional layers as a primary building block. Beyond helping C…

cs.LG2019

Extrapolating paths with graph neural networks

Jean-Baptiste Cordonnier, Andreas Loukas

We consider the problem of path inference: given a path prefix, i.e., a partially observed sequence of nodes in a graph, we want to predict which nodes are in the missing suffix. I…

cs.LG2018

Sparsified SGD with Memory

Sebastian U. Stich, Jean-Baptiste Cordonnier, Martin Jaggi

Huge scale machine learning problems are nowadays tackled by distributed optimization algorithms, i.e. algorithms that leverage the compute power of many devices for training. The…