A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.
Study confirms fractional norms and quasinorms do not help overcome curse of dimensionality.
Proves analyticity of quasinormal modes in Kerr and Kerr-de Sitter spacetimes.
New methods show quasinormal modes can be defined using various stationary Killing vectors.
Proves wave equation solutions in Kerr-de Sitter spacetime have specific asymptotic expansions.
We provide a novel analysis of low-rank tensor completion based on hypergraph expanders. As a proxy for rank, we minimize the max-quasinorm of the tensor, which generalizes the max-norm for matrices. Our analysis is deterministic and shows that the number of samples required to approximately recover an order- tensor…
In a previous paper we obtained formulae for the volume of a causal diamond or Alexandrov open set whose duration is short compared with the curvature scale. In the present paper we obtain asymptotic formulae valid when the point recedes to the future boundary of an asymp…
The -nearest neighbour (-NN) classifier is one of the oldest and most important supervised learning algorithms for classifying datasets. Traditionally the Euclidean norm is used as the distance for the -NN classifier. In this thesis we investigate the use of alternative distances for the -NN classifier. We …
Source imaging based on magnetoencephalography (MEG) and electroencephalography (EEG) allows for the non-invasive analysis of brain activity with high temporal and good spatial resolution. As the bioelectromagnetic inverse problem is ill-posed, constraints are required. For the analysis of evoked brain activity, spatia…
The paper improves tensor completion bounds using spectral gap.
This paper finds sparsest ReLU networks for interpolating data.
Most of machine learning approaches have stemmed from the application of minimizing the mean squared distance principle, based on the computationally efficient quadratic optimization methods. However, when faced with high-dimensional and noisy data, the quadratic error functionals demonstrated many weaknesses including…