7 citations · 20 across the 6 of their papers we have counts for
6 papers
Distribution-Conditioned Transport
Nic Fishman, Gokul Gowri, Paolo L. B. Fischer +3
Learning a transport model that maps a source distribution to a target distribution is a canonical problem in machine learning, but scientific applications increasingly require mod…
Count Bridges enable Modeling and Deconvolving Transcriptomic Data
Nic Fishman, Gokul Gowri, Tanush Kumar +4
Many modern biological assays, including RNA sequencing, yield integer-valued counts that reflect the number of molecules detected. These measurements are often not at the desired…
Metropolis Sampling for Constrained Diffusion Models
Nic Fishman, Leo Klarner, Emile Mathieu +2
Denoising diffusion models have recently emerged as the predominant paradigm for generative modelling on image domains. In addition, their extension to Riemannian manifolds has fac…
Diffusion Models for Constrained Domains
Nic Fishman, Leo Klarner, Valentin De Bortoli +2
Denoising diffusion models are a novel class of generative algorithms that achieve state-of-the-art performance across a range of domains, including image generation and text-to-im…
Human mobility networks reveal increased segregation in large cities
Hamed Nilforoshan, Wenli Looi, Emma Pierson +7
A long-standing expectation is that large, dense, and cosmopolitan areas support socioeconomic mixing and exposure between diverse individuals. It has been difficult to assess this…
Should attention be all we need? The epistemic and ethical implications of unification in machine learning
Nic Fishman, Leif Hancox-Li
"Attention is all you need" has become a fundamental precept in machine learning research. Originally designed for machine translation, transformers and the attention mechanisms th…