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.
A new method adds pseudo-data to tensor decomposition to improve accuracy and enforce various regularizations.
Paper proposes a novel method to improve matrix completion with median loss for large datasets.
A new method improves continual learning by replaying pseudo data and using orthogonal weight modification.
Develops a generic approach for stable model distillation.
We introduce a variational Bayesian neural network where the parameters are governed via a probability distribution on random matrices. Specifically, we employ a matrix variate Gaussian \cite{gupta1999matrix} parameter posterior distribution where we explicitly model the covariance among the input and output dimensions…
Model learns and generalizes new concepts efficiently from few labeled instances.
Gaussian processes (GPs) are flexible distributions over functions that enable high-level assumptions about unknown functions to be encoded in a parsimonious, flexible and general way. Although elegant, the application of GPs is limited by computational and analytical intractabilities that arise when data are sufficien…
Proposes a method for stable variable selection in high-dimensional data.