arXiv research

Develops a new model for cross-currency derivatives pricing.

Study on spin random fields using chaos decomposition for cosmic microwave background modeling.

Recent studies have suggested that the cognitive process of the human brain is realized as probabilistic inference and can be further modeled by probabilistic graphical models like Markov random fields. Nevertheless, it remains unclear how probabilistic inference can be implemented by a network of spiking neurons in th…

Paper calculates KL divergence for isotropic Gaussian-Markov fields.

Study of cosmic microwave background polarization using spin random fields.

New method speeds up sampling of Markov random fields.

Random Fourier features model reconstructs wind fields from sparse measurements.

A new method uses SPDEs to efficiently model random fields on complex domains.

New framework models neural systems with random architecture on manifolds.

The dominant language models (LMs) such as n-gram and neural network (NN) models represent sentence probabilities in terms of conditionals. In contrast, a new trans-dimensional random field (TRF) LM has been recently introduced to show superior performances, where the whole sentence is modeled as a random field. In thi…

Study a risk model with tree-structured Poisson-Markov random field for rainfall events.

This is a technical report which explores the estimation methodologies on hyper-parameters in Markov Random Field and Gaussian Hidden Markov Random Field. In first section, we briefly investigate a theoretical framework on Metropolis-Hastings algorithm. Next, by using MH algorithm, we simulate the data from Ising model…

The aim of this short note is to draw attention to a method by which the partition function and marginal probabilities for a certain class of random fields on complete graphs can be computed in polynomial time. This class includes Ising models with homogeneous pairwise potentials but arbitrary (inhomogeneous) unary pot…

DiAL uses Bayesian Dirichlet random fields for active learning with sparse labels.

The paper characterizes the geometry and topology of spin random fields.

New tree-structured Markov fields with Poisson marginals for counting variables.

Study finds Calabi-Yau models' operator spectra match random matrix theory.

Enhances GNN robustness during inference using Conditional Random Fields.

New method calculates geodesic distances in Gaussian random field manifolds.

Given a Gaussian Markov random field, we consider the problem of selecting a subset of variables to observe which minimizes the total expected squared prediction error of the unobserved variables. We first show that finding an exact solution is NP-hard even for a restricted class of Gaussian Markov random fields, calle…

Study on Gaussian random fields' singularities on manifolds.

CMRFs extend PGMs for topological data, capturing both conditional and marginal dependencies.

Estimates binary labels from dependent data using Markov Random Fields.

Bayesian method selects interacting regions in Markov models.

Abstract: Generalizes SGMs to infinite-dimensional Hilbertian setting.

Investigates multifractal scaling in critical dynamics of random surfaces.

In this paper we model the loss function of high-dimensional optimization problems by a Gaussian random field, or equivalently a Gaussian process. Our aim is to study gradient descent in such loss functions or energy landscapes and compare it to results obtained from real high-dimensional optimization problems such as …

Existence of strong randomized equilibria in mean-field games with common noise.

Deep GMRFs improve spatial data modeling and prediction.

Study loop corrections in random feature models affecting training and test errors.

We consider the structure learning problem for graphical models that we call loosely connected Markov random fields, in which the number of short paths between any pair of nodes is small, and present a new conditional independence test based algorithm for learning the underlying graph structure. The novel maximization …

CRF model improves protein secondary structure prediction.

Privacy constraints affect learning Markov Random Fields differently.

LC-CRFs are equivalent to HMMs, and MPM/MAP classifiers can be reformulated as CRFs.

Random Gaussian fields on 4D Riemannian manifolds with conformal invariance.

Loopy belief propagation (LBP), which is equivalent to the Bethe approximation in statistical mechanics, is a message-passing-type inference method that is widely used to analyze systems based on Markov random fields (MRFs). In this paper, we propose a message-passing-type method to analytically evaluate the quenched a…

A number of recent papers have provided evidence that practical design questions about neural networks may be tackled theoretically by studying the behavior of random networks. However, until now the tools available for analyzing random neural networks have been relatively ad-hoc. In this work, we show that the distrib…

Dual random fields improve mineral potential predictions.

We describe the first known mean-field study of landing probabilities for random walks on hypergraphs. In particular, we examine clique-expansion and tensor methods and evaluate their mean-field characteristics over a class of random hypergraph models for the purpose of seed-set community expansion. We describe paramet…

Integrates MRF into multimodal VAE for better complex intermodal interactions.

We consider the traffic data reconstruction problem. Suppose we have the traffic data of an entire city that are incomplete because some road data are unobserved. The problem is to reconstruct the unobserved parts of the data. In this paper, we propose a new method to reconstruct incomplete traffic data collected from …

We introduce a new embarrassingly parallel parameter learning algorithm for Markov random fields with untied parameters which is efficient for a large class of practical models. Our algorithm parallelizes naturally over cliques and, for graphs of bounded degree, its complexity is linear in the number of cliques. Unlike…

Method quantifies uncertainties in complex MRF models.

We consider the problem of learning the structure of Ising models (pairwise binary Markov random fields) from i.i.d. samples. While several methods have been proposed to accomplish this task, their relative merits and limitations remain somewhat obscure. By analyzing a number of concrete examples, we show that low-comp…

A scalable deep GMRF model for general graphs improves predictions and uncertainty estimates.

We model the term structure of the forward default intensity and the default density by using Lévy random fields, which allow us to consider the credit derivatives with an after-default recovery payment. As applications, we study the pricing of a defaultable bond and represent the pricing kernel as the unique solution …

To improve the classification performance in the context of hyperspectral image processing, many works have been developed based on two common strategies, namely the spatial-spectral information integration and the utilization of neural networks. However, both strategies typically require more training data than the cl…

Hidden Markov Chains and Linear-chain CRFs are equivalent.