4 papers
Random Process Flow Matching: Generative Implicit Representations of Multivariate Random Fields
Julien Lalanne, David Picard, Lionel Boillot +3
Generative modeling provides a powerful framework for learning data distributions. These models initially relied on probabilistic methods such as Gaussian Processes (GP) for uncert…
PoM: A Linear-Time Replacement for Attention with the Polynomial Mixer
David Picard, Nicolas Dufour, Lucas Degeorge +14
This paper introduces the Polynomial Mixer (PoM), a novel token mixing mechanism with linear complexity that serves as a drop-in replacement for self-attention. PoM aggregates inpu…
Closing the Performance Gap in Generative Recommenders with Collaborative Tokenization and Efficient Modeling
Simon Lepage, Jeremie Mary, David Picard
Recent work has explored generative recommender systems as an alternative to traditional ID-based models, reframing item recommendation as a sequence generation task over discrete…
Markov Chain Estimation with In-Context Learning
Simon Lepage, Jeremie Mary, David Picard
We investigate the capacity of transformers to learn algorithms involving their context while solely being trained using next token prediction. We set up Markov chains with random…