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Continuous diffusion for categorical data
Sander Dieleman, Laurent Sartran, Arman Roshannai +11
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement. Their success hinges…
Self-conditioned Embedding Diffusion for Text Generation
Robin Strudel, Corentin Tallec, Florent Altché +8
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation? To circumvent the discrete nature of text data, we can si…
Categorical SDEs with Simplex Diffusion
Pierre H. Richemond, Sander Dieleman, Arnaud Doucet
Diffusion models typically operate in the standard framework of generative modelling by producing continuously-valued datapoints. To this end, they rely on a progressive Gaussian s…
General-purpose, long-context autoregressive modeling with Perceiver AR
Curtis Hawthorne, Andrew Jaegle, Cătălina Cangea +12
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression. However, the most commonly u…