221 citations · 474 across the 8 of their papers we have counts for
9 papers
Unpaired RGB-Thermal Gaussian-Splatting Using Visual Geometric Transformers
Jean Cordonnier, Chenghao Xu, Olga Fink +1
Multi-modal novel view synthesis (NVS) combining RGB and thermal imagery enables precise 3D scene reconstruction with visual and thermal information. However, existing methods typi…
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…
Attention is Not All You Need: Pure Attention Loses Rank Doubly Exponentially with Depth
Yihe Dong, Jean-Baptiste Cordonnier, Andreas Loukas
Attention-based architectures have become ubiquitous in machine learning, yet our understanding of the reasons for their effectiveness remains limited. This work proposes a new way…
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…
Multi-Head Attention: Collaborate Instead of Concatenate
Jean-Baptiste Cordonnier, Andreas Loukas, Martin Jaggi
Attention layers are widely used in natural language processing (NLP) and are beginning to influence computer vision architectures. Training very large transformer models allowed s…
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…
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…
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…
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…