arXiv research

Paper proves spectral filters can be transferred between graphs.

Paper learns to rotate filters for group convolutions.

NBF combines deep learning with classical filtering for better belief tracking.

Gradient filters track moving parameters under noisy data and misspecification.

Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.

HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.

We introduce the variational filtering EM algorithm, a simple, general-purpose method for performing variational inference in dynamical latent variable models using information from only past and present variables, i.e. filtering. The algorithm is derived from the variational objective in the filtering setting and cons…

We examine some differential geometric approaches to finding approximate solutions to the continuous time nonlinear filtering problem. Our primary focus is a new projection method for the optimal filter infinite dimensional Stochastic Partial Differential Equation (SPDE), based on the direct L2 metric and on a family o…

New Gibbs sampling reduces GLMB filtering complexity to linear time.

Prunes CNNs by removing redundant filters with provable guarantees.

AFLite filters dataset biases to improve model generalization.

We simplify Bayesian filtering by framing it as optimization, making it practical for high-dimensional systems.

Paper presents a robust Kalman filter for state estimation.

We generate transformation-invariant CNNs using context-aware filters.

This article studies hypoellipticity on general filtered manifolds. We extend the Rockland criterion to a pseudodifferential calculus on filtered manifolds, construct a parametrix and describe its precise analytic structure. We use this result to study Rockland sequences, a notion generalizing elliptic sequences to fil…

DNNs improve SIMP method but not spatially invariant, study shows.

We present a general probabilistic perspective on Gaussian filtering and smoothing. This allows us to show that common approaches to Gaussian filtering/smoothing can be distinguished solely by their methods of computing/approximating the means and covariances of joint probabilities. This implies that novel filters and …

Removing or filtering outliers and mislabeled instances prior to training a learning algorithm has been shown to increase classification accuracy. A popular approach for handling outliers and mislabeled instances is to remove any instance that is misclassified by a learning algorithm. However, an examination of which l…

Study of filtering and smoothing in submanifolds of Euclidean space.

Latent FxLMS accelerates ANC by adapting along low-dimensional filter weights.

Convolutional neural networks (CNNs) have recently emerged as a popular building block for natural language processing (NLP). Despite their success, most existing CNN models employed in NLP share the same learned (and static) set of filters for all input sentences. In this paper, we consider an approach of using a smal…

Filtering data with a pre-trained model improves multimodal contrastive learning performance.

New filter bank regularization improves DCNNs by incorporating image priors.

This study improves state estimation for nonlinear systems using conditional normalizing flows.

New framework improves robust inference in HMMs under model misspecification.

Filtering is a general name for inferring the states of a dynamical system given observations. The most common filtering approach is Gaussian Filtering (GF) where the distribution of the inferred states is a Gaussian whose mean is an affine function of the observations. There are two restrictions in this model: Gaussia…

A new flow-based Bayesian filter tackles high-dimensional nonlinear stochastic systems.

We construct Hodge filtered cohomology groups for complex manifolds that combine the topological information of generalized cohomology theories with geometric data of Hodge filtered holomorphic forms. This theory provides a natural generalization of Deligne cohomology. For smooth complex algebraic varieties, we show th…

A guide to using low-pass graph filters for network data.

Develops inverse unscented Kalman filter for non-linear systems.

Convergence of the Kalman filter is best analyzed by studying the contraction of the Riccati map in the space of positive definite (covariance) matrices. In this paper, we explore how this contraction property relates to a more fundamental non-expansiveness property of filtering maps in the space of probability distrib…

New complexes derived from any filtered cochain complex compute the same cohomology.

A new ML-based filter improves data assimilation for nonlinear systems.

Three situations in which filtering theory is used in mathematical finance are illustrated at different levels of detail. The three problems originate from the following different works: 1) On estimating the stochastic volatility model from observed bilateral exchange rate news, by R. Mahieu, and P. Schotman; 2) A stat…

This paper introduces the kernel mixture network, a new method for nonparametric estimation of conditional probability densities using neural networks. We model arbitrarily complex conditional densities as linear combinations of a family of kernel functions centered at a subset of training points. The weights are deter…

New deep learning method approximates Benes filter model.

Develops inverse extended Kalman filter for predicting adversarial steps.

A new filter adapts to heavy-tailed data without tuning, improving performance in challenging conditions.

Novel filtering method for high-dimensional chaotic systems.

Graph Kalman filters adapt classical filters to graph data.

OBF optimally filters features under independent Gaussian models.

We introduce filtered cohomologies of differential forms on symplectic manifolds. They generalize and include the cohomologies discussed in Paper I and II as a subset. The filtered cohomologies are finite-dimensional and can be associated with differential elliptic complexes. Algebraically, we show that the filtered co…

Particle filtering is a powerful approach to sequential state estimation and finds application in many domains, including robot localization, object tracking, etc. To apply particle filtering in practice, a critical challenge is to construct probabilistic system models, especially for systems with complex dynamics or r…

EnFF uses flows to speed up DA in high dimensions.

Confidence-based filtering reveals latent structure in diffusion models.

WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.

This study uses neural networks to approximate Bayesian filtering problems.

In this paper we examine the effect of applying ensemble learning to the performance of collaborative filtering methods. We present several systematic approaches for generating an ensemble of collaborative filtering models based on a single collaborative filtering algorithm (single-model or homogeneous ensemble). We pr…