1 citations · 1 across the 5 of their papers we have counts for
5 papers
Latent Stochastic Interpolants
Saurabh Singh, Dmitry Lagun
Stochastic Interpolants (SI) is a powerful framework for generative modeling, capable of flexibly transforming between two probability distributions. However, its use in jointly op…
Discretized Approximate Ancestral Sampling
Alfredo De la Fuente, Saurabh Singh, Jona Ballé
The Fourier Basis Density Model (FBM) was recently introduced as a flexible probability model for band-limited distributions, i.e. ones which are smooth in the sense of having a ch…
Stochastic Sampling from Deterministic Flow Models
Saurabh Singh, Ian Fischer
Deterministic flow models, such as rectified flows, offer a general framework for learning a deterministic transport map between two distributions, realized as the vector field for…
Fourier Head: Helping Large Language Models Learn Complex Probability Distributions
Nate Gillman, Daksh Aggarwal, Michael Freeman +2
As the quality of large language models has improved, there has been increased interest in using them to model non-linguistic tokens. For example, the Decision Transformer recasts…
Fourier Basis Density Model
Alfredo De la Fuente, Saurabh Singh, Johannes Ballé
We introduce a lightweight, flexible and end-to-end trainable probability density model parameterized by a constrained Fourier basis. We assess its performance at approximating a r…