3 citations · 3 across the 3 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2024
HiPPO-Prophecy: State-Space Models can Provably Learn Dynamical Systems in Context
Federico Arangath Joseph, Kilian Konstantin Haefeli, Noah Liniger +1
This work explores the in-context learning capabilities of State Space Models (SSMs) and presents, to the best of our knowledge, the first theoretical explanation of a possible und…
cs.LG2022★ 3 cited
Diffusion Models for Graphs Benefit From Discrete State Spaces
Kilian Konstantin Haefeli, Karolis Martinkus, Nathanaël Perraudin +1
Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the genera…