36 citations · 128 across the 27 of their papers we have counts for
5 papers · 1 filter
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem +3
Precision measurements and new physics searches at the Large Hadron Collider require efficient simulations of particle propagation and interactions within the detectors. The most c…
Relating Regularization and Generalization through the Intrinsic Dimension of Activations
Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini +1
Given a pair of models with similar training set performance, it is natural to assume that the model that possesses simpler internal representations would exhibit better generaliza…
Verifying the Union of Manifolds Hypothesis for Image Data
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross +2
Deep learning has had tremendous success at learning low-dimensional representations of high-dimensional data. This success would be impossible if there was no hidden low-dimension…
Neural Implicit Manifold Learning for Topology-Aware Density Estimation
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini +1
Natural data observed in is often constrained to an -dimensional manifold , where . This work focuses on the task of building theoretically pr…
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell +1
Likelihood-based, or explicit, deep generative models use neural networks to construct flexible high-dimensional densities. This formulation directly contradicts the manifold hypot…