2 citations · 5 across the 16 of their papers we have counts for
6 papers · 1 filter
Towards Understanding the Universality of Transformers for Next-Token Prediction
Michael E. Sander, Gabriel Peyré
Causal Transformers are trained to predict the next token for a given context. While it is widely accepted that self-attention is crucial for encoding the causal structure of seque…
Transformers are Universal In-context Learners
Takashi Furuya, Maarten V. de Hoop, Gabriel Peyré
Transformers are deep architectures that define "in-context mappings" which enable predicting new tokens based on a given set of tokens (such as a prompt in NLP applications or a s…
Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows
Sibylle Marcotte, Rémi Gribonval, Gabriel Peyré
Conservation laws are well-established in the context of Euclidean gradient flow dynamics, notably for linear or ReLU neural network training. Yet, their existence and principles f…
Understanding the training of infinitely deep and wide ResNets with Conditional Optimal Transport
Raphaël Barboni, Gabriel Peyré, François-Xavier Vialard
We study the convergence of gradient flow for the training of deep neural networks. If Residual Neural Networks are a popular example of very deep architectures, their training con…
Enhancing Hypergradients Estimation: A Study of Preconditioning and Reparameterization
Zhenzhang Ye, Gabriel Peyré, Daniel Cremers +1
Bilevel optimization aims to optimize an outer objective function that depends on the solution to an inner optimization problem. It is routinely used in Machine Learning, notably f…
How do Transformers perform In-Context Autoregressive Learning?
Michael E. Sander, Raja Giryes, Taiji Suzuki +2
Transformers have achieved state-of-the-art performance in language modeling tasks. However, the reasons behind their tremendous success are still unclear. In this paper, towards a…