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.
Convolutional GANs favor low spatial frequencies, affecting fine detail generation.
New CV method reduces bias in spatial prediction models.
CNNs can develop blind spots due to uneven padding in feature maps.
New method preserves GCM spatial dependencies for better climate projections.
Convolutional neural networks are among the most successful architectures in deep learning with this success at least partially attributable to the efficacy of spatial invariance as an inductive bias. Locally connected layers, which differ from convolutional layers only in their lack of spatial invariance, usually perf…
Generative model improves wind field downscaling from coarse climate models.
Gradient boosting for spatial regression models improves prediction accuracy.
Proposes a transformer model with geostatistical inductive bias for spatio-temporal forecasting.
Local convolutions bias neural networks towards high-frequency adversarial examples.
Forest-guided smoothing uses random forest outputs for interpretable local smoothers.
Machine-learning algorithms have gained popularity in recent years in the field of ecological modeling due to their promising results in predictive performance of classification problems. While the application of such algorithms has been highly simplified in the last years due to their well-documented integration in co…
Paper presents a method for geographic ratemaking using spatial embeddings.
Selective relevance method improves motion explainability in 3D activity recognition models.
Parametric images provide insight into the spatial distribution of physiological parameters, but they are often extremely noisy, due to low SNR of tomographic data. Direct estimation from projections allows accurate noise modeling, improving the results of post-reconstruction fitting. We propose a method, which we name…
Gradient boosting algorithm for spatial panel models improves estimation in high-dimensional settings.
Flow prediction (e.g., crowd flow, traffic flow) with features of spatial-temporal is increasingly investigated in AI research field. It is very challenging due to the complicated spatial dependencies between different locations and dynamic temporal dependencies among different time intervals. Although measurements of …
Framework combines HMM and MTGCN for spatiotemporal causal inference in clinical data.
Method reduces model bias in water temperature prediction using physics-guided GNNs.
New models reduce regional inequality by adjusting exchange range and asset distribution bias.
NN-GPR improves climate model predictions by preserving fine-scale spatial information.
Deep learning has proven to yield fast and accurate predictions of quantum-chemical properties to accelerate the discovery of novel molecules and materials. As an exhaustive exploration of the vast chemical space is still infeasible, we require generative models that guide our search towards systems with desired proper…
Several applications of Reinforcement Learning suffer from instability due to high variance. This is especially prevalent in high dimensional domains. Regularization is a commonly used technique in machine learning to reduce variance, at the cost of introducing some bias. Most existing regularization techniques focus o…
Deep convolutional networks can be understood through kernel methods, providing insights into their inductive bias.
STAS selects optimal spatio-temporal scales for bias correction in precipitation forecasts.
Disentangled representation learning finds compact, independent and easy-to-interpret factors of the data. Learning such has been shown to require an inductive bias, which we explicitly encode in a generative model of images. Specifically, we propose a model with two latent spaces: one that represents spatial transform…
Active learning has long been a topic of study in machine learning. However, as increasingly complex and opaque models have become standard practice, the process of active learning, too, has become more opaque. There has been little investigation into interpreting what specific trends and patterns an active learning st…
We provide a computationally and statistically efficient method for estimating the parameters of a stochastic covariance model observed on a regular spatial grid in any number of dimensions. Our proposed method, which we call the Debiased Spatial Whittle likelihood, makes important corrections to the well-known Whittle…
Developed a diffusion model on spherical data, addressing geometric and stochastic challenges.
Package {mlr3spatiotempcv} simplifies spatiotemporal resampling methods in R.
Mapping forest aboveground biomass (AGB) has become an important task, particularly for the reporting of carbon stocks and changes. AGB can be mapped using synthetic aperture radar data (SAR) or passive optical data. However, these data are insensitive to high AGB levels (\textgreater{}150 Mg/ha, and \textgreater{}300 …
We study the challenges of applying deep learning to gene expression data. We find experimentally that there exists non-linear signal in the data, however is it not discovered automatically given the noise and low numbers of samples used in most research. We discuss how gene interaction graphs (same pathway, protein-pr…
FreST Loss decorrelates spatio-temporal dependencies in graph signals.
We consider calculation of capital requirements when the underlying economic scenarios are determined by simulatable risk factors. In the respective nested simulation framework, the goal is to estimate portfolio tail risk, quantified via VaR or TVaR of a given collection of future economic scenarios representing factor…
Weather2vec learns representations to adjust for non-local confounding in air pollution studies.
SA-BCP combines long-term and local evidence for efficient, adaptive online prediction.
FPGs use structure to improve policy learning in complex tasks.
We introduce the Convolutional Conditional Neural Process (ConvCNP), a new member of the Neural Process family that models translation equivariance in the data. Translation equivariance is an important inductive bias for many learning problems including time series modelling, spatial data, and images. The model embeds …
Spatial blind source separation simplifies multivariate spatial prediction.
The paper introduces groupoid racks for spatial surfaces.
Machine learning algorithms find frequent application in spatial prediction of biotic and abiotic environmental variables. However, the characteristics of spatial data, especially spatial autocorrelation, are widely ignored. We hypothesize that this is problematic and results in models that can reproduce training data …
STICC clusters geographic objects considering both spatial contiguity and attributes.
Algorithm learns stochastic system dynamics from data.
Unstructured data from diverse sources, such as social media and aerial imagery, can provide valuable up-to-date information for intelligent situation assessment. Mining these different information sources could bring major benefits to applications such as situation awareness in disaster zones and mapping the spread of…
Neural network language models (NNLMs) have achieved ever-improving accuracy due to more sophisticated architectures and increasing amounts of training data. However, the inductive bias of these models (formed by the distributional hypothesis of language), while ideally suited to modeling most running text, results in …
Spatial understanding is a fundamental problem with wide-reaching real-world applications. The representation of spatial knowledge is often modeled with spatial templates, i.e., regions of acceptability of two objects under an explicit spatial relationship (e.g., "on", "below", etc.). In contrast with prior work that r…
Defines non-parabolic curves in spatial hybrid space with applications.
A framework converts spatial data into embeddings for insurance risk modelling.
This study analyses, through cross-section estimation methods, the influence of spatial effects in the conditional product convergence in the parishes' economies of mainland Portugal between 1991 and 2001 (the last year with data available for this spatial disaggregation level). To analyse the data, Moran's I statistic…