no code implementations • 22 Apr 2024 • Amirmojtaba Sabour, Sanja Fidler, Karsten Kreis
Diffusion models (DMs) have established themselves as the state-of-the-art generative modeling approach in the visual domain and beyond.
no code implementations • 22 Jan 2024 • Koichi Namekata, Amirmojtaba Sabour, Sanja Fidler, Seung Wook Kim
Diffusion models have recently received increasing research attention for their remarkable transfer abilities in semantic segmentation tasks.
no code implementations • 7 Oct 2021 • Yassaman Ommi, Matin Yousefabadi, Faezeh Faez, Amirmojtaba Sabour, Mahdieh Soleymani Baghshah, Hamid R. Rabiee
With an increase in the number of applications where data is represented as graphs, the problem of graph generation has recently become a hot topic.
no code implementations • NeurIPS 2021 • Giorgi Nadiradze, Amirmojtaba Sabour, Peter Davies, Shigang Li, Dan Alistarh
Perhaps surprisingly, we show that a variant of SGD called \emph{SwarmSGD} still converges in this setting, even if \emph{non-blocking communication}, \emph{quantization}, and \emph{local steps} are all applied \emph{in conjunction}, and even if the node data distributions and underlying graph topology are both \emph{heterogenous}.
no code implementations • 25 Sep 2019 • Giorgi Nadiradze, Amirmojtaba Sabour, Aditya Sharma, Ilia Markov, Vitaly Aksenov, Dan Alistarh.
We prove that, under standard assumptions, SGD can converge even in this extremely loose, decentralized setting, for both convex and non-convex objectives.