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.
We define a pseudo-inverse for line graphs using linear integer programming.
We focus on the maximum regularization parameter for anisotropic total-variation denoising. It corresponds to the minimum value of the regularization parameter above which the solution remains constant. While this value is well know for the Lasso, such a critical value has not been investigated in details for the total…
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks. The method is based on linear discriminant analysis and provides Bayes optimal single point estimates for the weight values.
The popularity of high and ultra-high definition displays has led to the need for methods to improve the quality of videos already obtained at much lower resolutions. Current Video Super-Resolution methods are not robust to mismatch between training and testing degradation models since they are trained against a single…
A bridge between continuous signals and discrete Ising spins for associative memory.
New algorithms handle online prediction with bandit and delayed feedback, improving regret bounds.
The inverse-free extreme learning machine (ELM) algorithm proposed in [4] was based on an inverse-free algorithm to compute the regularized pseudo-inverse, which was deduced from an inverse-free recursive algorithm to update the inverse of a Hermitian matrix. Before that recursive algorithm was applied in [4], its impr…
Discrete Green's functions are the inverses or pseudo-inverses of combinatorial Laplacians. We present compact formulas for discrete Green's functions, in terms of the eigensystems of corresponding Laplacians, for products of regular graphs with or without boundary. Explicit formulas are derived for the cycle, torus, a…
Paper introduces a taxonomy of reduction matrices for more efficient graph coarsening.
In this work we construct an optimal shrinkage estimator for the precision matrix in high dimensions. We consider the general asymptotics when the number of variables and the sample size so that . The precision matrix is estimated directly, wit…
Two new inverse-free ELM algorithms for incremental and decremental learning are proposed.
Unified method for deriving ridgelet transforms for various neural network architectures.
EnKG solves inverse problems without derivatives, using diffusion models.
The paper extends consistency results for sequential design strategies to vector-valued Gaussian processes.
Recent developments in the field of deep learning have motivated many researchers to apply these methods to problems in quantum information. Torlai and Melko first proposed a decoder for surface codes based on neural networks. Since then, many other researchers have applied neural networks to study a variety of problem…
Unified theory and debiasing framework for random oblique projections in high dimensions.
Isometry regularizer improves autoencoder performance on manifold learning.
Spectral sparsification improves Laplacian-constrained graph learning.
New feature selection method DRPT reduces genomic datasets by removing irrelevant features and detecting correlations.