7 citations · 8 across the 6 of their papers we have counts for
4 papers · 1 filter
Memorization and Regularization in Generative Diffusion Models
Ricardo Baptista, Agnimitra Dasgupta, Nikola B. Kovachki +2
Diffusion models have emerged as a powerful framework for generative modeling. At the heart of the methodology is score matching: learning gradients of families of log-densities fo…
Neural Approximate Mirror Maps for Constrained Diffusion Models
Berthy T. Feng, Ricardo Baptista, Katherine L. Bouman
Diffusion models excel at creating visually-convincing images, but they often struggle to meet subtle constraints inherent in the training data. Such constraints could be physics-b…
Structured Neural Networks for Density Estimation and Causal Inference
Asic Q. Chen, Ruian Shi, Xiang Gao +2
Injecting structure into neural networks enables learning functions that satisfy invariances with respect to subsets of inputs. For instance, when learning generative models using…
Codiscovering graphical structure and functional relationships within data: A Gaussian Process framework for connecting the dots
Théo Bourdais, Pau Batlle, Xianjin Yang +3
Most problems within and beyond the scientific domain can be framed into one of the following three levels of complexity of function approximation. Type 1: Approximate an unknown f…