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Aaron Lou

Aaron Lou

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Language Modeling by Estimating the Ratios of the Data Distribution

Modern large language models (like ChatGPT) learn to generate new samples by modeling the data distribution of natural text. However, the underlying methodology has largely remained stagnant over the last century: although different architectures have been developed, models are all based on autoregressive modeling (i.e. next token prediction). In this blog post, I will talk about our work on Score…

Reflected Diffusion Models

Diffusion models are trained to reverse a stochastic process through score matching. However, a lot of diffusion models rely on a small but critical implementation detail called thresholding. Thresholding projects the sampling process to the data support after each discretized diffusion step, stabilizing generation at the cost of breaking the theoretical framework. Interestingly, as one limits the…