16 citations · 26 across the 15 of their papers we have counts for
6 papers · 1 filter
The Fenchel-Young Generalization of Bayesian Principles
Sophia Sklaviadis, Thomas Moellenhoff, Andre Martins +2
Conjugate Bayes plays a key role in machine learning, ensuring computationally simple posterior updates. We generalize the variational principle behind this computation by using th…
Modern Hopfield Networks with Continuous-Time Memories
Saul Santos, António Farinhas, Daniel C. McNamee +1
Recent research has established a connection between modern Hopfield networks (HNs) and transformer attention heads, with guarantees of exponential storage capacity. However, these…
Non-Exchangeable Conformal Risk Control
António Farinhas, Chrysoula Zerva, Dennis Ulmer +1
Split conformal prediction has recently sparked great interest due to its ability to provide formally guaranteed uncertainty sets or intervals for predictions made by black-box neu…
Sparse Communication via Mixed Distributions
António Farinhas, Wilker Aziz, Vlad Niculae +1
Neural networks and other machine learning models compute continuous representations, while humans communicate mostly through discrete symbols. Reconciling these two forms of commu…
Sparse Continuous Distributions and Fenchel-Young Losses
André F. T. Martins, Marcos Treviso, António Farinhas +4
Exponential families are widely used in machine learning, including many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical distr…
Sparse and Continuous Attention Mechanisms
André F. T. Martins, António Farinhas, Marcos Treviso +3
Exponential families are widely used in machine learning; they include many distributions in continuous and discrete domains (e.g., Gaussian, Dirichlet, Poisson, and categorical di…