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.
Geospatial ML models need special evaluation methods due to their unique challenges.
Geospatial framework assesses climate risks for California's banking and exposed sectors.
Gaussian processes model geospatial trajectories with uncertainty.
State-of-the-art deep learning methods have shown a remarkable capacity to model complex data domains, but struggle with geospatial data. In this paper, we introduce SpaceGAN, a novel generative model for geospatial domains that learns neighbourhood structures through spatial conditioning. We propose to enhance spatial…
In this paper, we evaluate the accuracy of deep learning approaches on geospatial vector geometry classification tasks. The purpose of this evaluation is to investigate the ability of deep learning models to learn from geometry coordinates directly. Previous machine learning research applied to geospatial polygon data …
SX-GeoTree improves spatially coherent explanations in geospatial regression trees.
Neural networks improve geospatial data analysis by relaxing linearity assumptions.
Model infers mineral locations from geospatial data, improving predictions with auxiliary data.
Fast variational Bayes methods improve geospatial data analysis speed and accuracy.
Geostatistical learning faces unique challenges due to spatial correlation and covariate shifts.
Localized CNNs improve geospatial wind forecasting.
Develops RF-GLS for binary geospatial data.
Deep learning models perform variably across continents/seasons in land cover mapping.
FFRK automatically extracts features for spatial interpolation without external variables.
Designing a covariance function that represents the underlying correlation is a crucial step in modeling complex natural systems, such as climate models. Geospatial datasets at a global scale usually suffer from non-stationarity and non-uniformly smooth spatial boundaries. A Gaussian process regression using a non-stat…
Proposes a neural network method to correct residual distortions in coordinate transformations.
Understanding intrinsic patterns and predicting spatiotemporal characteristics of cities require a comprehensive representation of urban neighborhoods. Existing works relied on either inter- or intra-region connectivities to generate neighborhood representations but failed to fully utilize the informative yet heterogen…
This study surveys methods for detecting outliers in spatial data.
New method improves stability of Gaussian process approximations.
The paper uses GIS data to predict urban sprawl.
This paper describes a hierarchical learning strategy for generating sparse representations of multivariate datasets. The hierarchy arises from approximation spaces considered at successively finer scales. A detailed analysis of stability, convergence and behavior of error functionals associated with the approximations…
We propose non-stationary spectral kernels for Gaussian process regression. We propose to model the spectral density of a non-stationary kernel function as a mixture of input-dependent Gaussian process frequency density surfaces. We solve the generalised Fourier transform with such a model, and present a family of non-…
Paper develops a new model for forecasting ocean currents.
Geospatial analysis lacks methods like the word vector representations and pre-trained networks that significantly boost performance across a wide range of natural language and computer vision tasks. To fill this gap, we introduce Tile2Vec, an unsupervised representation learning algorithm that extends the distribution…
Satellite imagery and remote sensing provide explanatory variables at relatively high resolutions for modeling geospatial phenomena, yet regional summaries are often desirable for analysis and actionable insight. In this paper, we propose a novel method of inducing spatial aggregations as a component of the machine lea…
We study learning problems in which the conditional distribution of the output given the input varies as a function of additional task variables. In varying-coefficient models with Gaussian process priors, a Gaussian process generates the functional relationship between the task variables and the parameters of this con…
This document serves to complement our website which was developed with the aim of exposing the students to Gaussian Processes (GPs). GPs are non-parametric Bayesian regression models that are largely used by statisticians and geospatial data scientists for modeling spatial data. Several open source libraries spanning …
Armed conflict has led to an unprecedented number of internally displaced persons (IDPs) - individuals who are forced out of their homes but remain within their country. IDPs often urgently require shelter, food, and healthcare, yet prediction of when large fluxes of IDPs will cross into an area remains a major challen…
Modeling supply chain disruptions from climate hazards with adaptive firms.
Post-Quantum Secure Federated DeFi for Inclusive Banking
Study improves paddy rice yield predictions in Peru using sparse regression and climatic variables.
Charities can increase donations by targeting optimal recipients.
This paper introduces CloudLSTM, a new branch of recurrent neural models tailored to forecasting over data streams generated by geospatial point-cloud sources. We design a Dynamic Point-cloud Convolution (DConv) operator as the core component of CloudLSTMs, which performs convolution directly over point-clouds and extr…
Kernel-Gradient Drifting improves generative modeling for non-Euclidean data.
Density-based clustering techniques are used in a wide range of data mining applications. One of their most attractive features con- sists in not making use of prior knowledge of the number of clusters that a dataset contains along with their shape. In this paper we propose a new algorithm named Linear DBSCAN (Lin-DBSC…
Graph Attention Networks predict power outage durations from natural disasters.
Local GP approach improves simulation efficiency for large datasets.
Model predicts missing boarding stops in smart card data.
Deep learning predicts crop prices with improved accuracy.
Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the…
New approach combines PCA and t-sne for better data analysis.
Magnitude-based features capture interactions between different entities in multispecies spatial data.
NN-GPR improves climate model predictions by preserving fine-scale spatial information.
Develops a theoretical framework for scalable Gaussian Process regression methods.
Applied Data Scientists throughout various industries are commonly faced with the challenging task of encoding high-cardinality categorical features into digestible inputs for machine learning algorithms. This paper describes a Bayesian encoding technique developed for WeWork's lead scoring engine which outputs the pro…
Proposes a deep neural network for spatial data regression.
In many research fields, the sizes of the existing datasets vary widely. Hence, there is a need for machine learning techniques which are well-suited for these different datasets. One possible technique is the self-organizing map (SOM), a type of artificial neural network which is, so far, weakly represented in the fie…
Mosquitoes are vectors of many human diseases. In particular, Aedes ægypti (Linnaeus) is the main vector for Chikungunya, Dengue, and Zika viruses in Latin America and it represents a global threat. Public health policies that aim at combating this vector require dependable and timely information, which is usually expe…