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cs.LG2025
Demystifying Diffusion Objectives: Reweighted Losses are Better Variational Bounds
Jiaxin Shi, Michalis K. Titsias
We derive a new theoretical interpretation of the reweighted losses that are widely used for training diffusion models. Our method is based on constructing a cascade of time-depend…
cs.LG2025
Learning-Order Autoregressive Models with Application to Molecular Graph Generation
Zhe Wang, Jiaxin Shi, Nicolas Heess +2
Autoregressive models (ARMs) have become the workhorse for sequence generation tasks, since many problems can be modeled as next-token prediction. While there appears to be a natur…