A new arXiv preprint examines using neural networks to represent ground-state electron charge density in real space. The work is motivated by the Hohenberg-Kohn theorem, which holds that ground-state density encodes all ground-state information about a many-electron system. The authors also study how well such learned models generalize.
A new arXiv paper addresses how the same 3D solid can be described by multiple boundary representations (B-reps) in CAD systems, which creates ambiguity for machine learning models. The authors propose learning canonical inputs so that neural networks operate on the underlying solid rather than the particular file encoding. This aims to make predictions consistent regardless of how a model was originally constructed.
A newly posted arXiv paper in cs.AI hypothesizes that the structure of phenomenal experience mirrors the first-order structure of physical interactions, mathematically captured by gradients or Jacobians. The authors develop this idea within an idealized world called Gradland, inhabited by neural networks. The work is a theoretical contribution to discussions of machine consciousness rather than an empirical study.
A revised arXiv paper challenges the widely held view that statistical methods outperform neural networks when generating differentially private tabular data. The authors argue that this conclusion glosses over cases involving densely correlated data, where neural approaches may be more effective. The submission is an updated version of previously posted research.