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.
New method estimates spatial weights matrix for lattice data, improving prediction accuracy.
A-BLINK speeds up Gaussian process covariance estimation.
In this paper, we propose a Ward-like hierarchical clustering algorithm including spatial/geographical constraints. Two dissimilarity matrices and are inputted, along with a mixing parameter . The dissimilarities can be non-Euclidean and the weights of the observations can be non-uniform. The fi…
New invariants distinguish spatial graphs not previously possible.
Proposes deep graph persistence to address neural persistence issues in deep learning.
New method improves convergence of spatial filters in neural networks.
New CV method reduces bias in spatial prediction models.
The paper addresses ill-conditioning in large spatial data, proposing solutions for prediction and likelihood estimation.
We investigate whether ResNet architectures can outperform more traditional Convolutional Neural Networks on the task of fine-grained vehicle classification. We train and test ResNet-18, ResNet-34 and ResNet-50 on the Comprehensive Cars dataset without pre-training on other datasets. We then modify the networks to use …
Infinite CNNs lose spatial correlations, but can be restored by correlated weights.
The key idea of variational auto-encoders (VAEs) resembles that of traditional auto-encoder models in which spatial information is supposed to be explicitly encoded in the latent space. However, the latent variables in VAEs are vectors, which can be interpreted as multiple feature maps of size 1x1. Such representations…
Our purpose in this paper is to apply some maximum principles in order to study the rigidity of complete spacelike hypersurfaces immersed in a spatially weighted generalized Robertson-Walker (GRW) spacetime, which is supposed to obey the so called strong null convergence condition. Under natural constraints on the weig…
A new neural approach for generating origin-destination matrices in ABMs.
A new framework enhances IDW models for complex industrial datasets.
WBCP improves conformal prediction for distribution shifts using weighted Dirichlet posteriors.
Survey on matrix hydrodynamics, a 2D fluid model.
A {\em balanced} spatial graph has an integer weight on each edge, so that the directed sum of the weights at each vertex is zero. We describe the Alexander module and polynomial for balanced spatial graphs (originally due to Kinoshita \cite{ki}), and examine their behavior under some common operations on the graph. We…
Proposes TS-NMF for 2D clustering, preserving spatial info.
GWRBoost improves GWR for better spatial relationship quantification.
In this paper, we extend a technique due to Romero, Rubio and Salamanca establishing sufficient conditions to guarantee the parabolicity of complete spacelike hypersurfaces immersed in a weighted generalized Robertson-Walker spacetime whose fiber has phi-parabolic universal Riemannian covering. As some applications of …
Proposes a GNN for multivariate time-series prediction with filtering.
Alexander polynomial equals spanning tree count at t=1.
MSFA clusters high-dimensional spatial data using spline-based covariance structures.
Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina as a relatively simple neuronal circuit. A retinal ganglion cell receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are requir…
Brain networks in fMRI are typically identified using spatial independent component analysis (ICA), yet mathematical constraints such as sparse coding and positivity both provide alternate biologically-plausible frameworks for generating brain networks. Non-negative Matrix Factorization (NMF) would suppress negative BO…
To better understand the spatial structure of large panels of economic and financial time series and provide a guideline for constructing semiparametric models, this paper first considers estimating a large spatial covariance matrix of the generalized -dependent and -mixing time series (with variables and …
A neighborhood homotopy is an equivalence relation on spatial graphs which is generated by crossing changes on the same component and neighborhood equivalence. We give a complete classification of all 2-component spatial graphs up to neighborhood homotopy by the elementary divisor of a linking matrix with respect to th…
Spatially weighted conformal prediction improves uncertainty quantification in house price models.
New framework models complex spatial data with basis functions and graphical vectors.
Airlines optimize fuel loading with better flight time predictions.
In a spatially embedded network, that is a network where nodes can be uniquely determined in a system of coordinates, links' weights might be affected by metric distances coupling every pair of nodes (dyads). In order to assess to what extent metric distances affect relationships (link's weights) in a spatially embedde…
This paper describes a versatile method that accelerates multichannel source separation methods based on full-rank spatial modeling. A popular approach to multichannel source separation is to integrate a spatial model with a source model for estimating the spatial covariance matrices (SCMs) and power spectral densities…
A2-SBNN models spatial data with copulas for non-Gaussian dependencies.
Unweighted matrix factorization can match or outperform weighted methods in recommender systems.
I propose and briefly define the concept of Urban Isobenefit Lines by using functions as easy as efficient, whose results can offer a rich tool to use into spatial equilibrium analysis involving cities. They are line joining urban points with equal level of positional advantage from city amenities. The results which on…
The paper analyzes how Gaussian kernel parameters affect posterior covariance in Gaussian processes.
SMM preserves matrix data structure for SVM classification.
A new model predicts financial volatility across firms using spatial correlations.
In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest is known to be structured in several ways at the same time. This premise has recently guided the research to the innovative and meaningful …
Spatial processes with nonstationary and anisotropic covariance structure are often used when modelling, analysing and predicting complex environmental phenomena. Such processes may often be expressed as ones that have stationary and isotropic covariance structure on a warped spatial domain. However, the warping functi…
Paper develops a new weighted low-rank matrix approximation technique.
We develop a machine learning approach to represent and analyze the underlying spatial structure that governs shot selection among professional basketball players in the NBA. Typically, NBA players are discussed and compared in an heuristic, imprecise manner that relies on unmeasured intuitions about player behavior. T…
Method selects number of communities in weighted networks.
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …
Convolutional sparse coding (CSC) can learn representative shift-invariant patterns from multiple kinds of data. However, existing CSC methods can only model noises from Gaussian distribution, which is restrictive and unrealistic. In this paper, we propose a general CSC model capable of dealing with complicated unknown…
Study evaluates thresholds for removing noise from DNN weights using random matrix theory.
Bayesian Empirical Bayes extends EB to complex structures using probabilistic symmetry.
We consider the problem of estimating a low-rank matrix from a noisy observed matrix. Previous work has shown that the optimal method depends crucially on the choice of loss function. In this paper, we use a family of weighted loss functions, which arise naturally for problems such as submatrix denoising, denoising wit…