36 citations · 40 across the 5 of their papers we have counts for
7 papers
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…
Rectangular Flows for Manifold Learning
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss +1
Normalizing flows are invertible neural networks with tractable change-of-volume terms, which allow optimization of their parameters to be efficiently performed via maximum likelih…
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner +4
Multi-goal reaching is an important problem in reinforcement learning needed to achieve algorithmic generalization. Despite recent advances in this field, current algorithms suffer…
Detecting anthropogenic cloud perturbations with deep learning
Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen +3
One of the most pressing questions in climate science is that of the effect of anthropogenic aerosol on the Earth's energy balance. Aerosols provide the `seeds' on which cloud drop…
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows
Rob Cornish, Anthony L. Caterini, George Deligiannidis +1
We show that normalising flows become pathological when used to model targets whose supports have complicated topologies. In this scenario, we prove that a flow must become arbitra…