arXiv research

The paper explores modifications to filter banks for speech recognition.

End-to-end CNN classifies environmental sounds from audio signals.

Model classifies environment sounds using multiple feature channels and attention mechanisms.

Paper proves spectral filters can be transferred between graphs.

A new SOHP filter improves trend estimation in economic time series.

Deep density methods improve filtering in high-dimensional systems.

The sophisticated structure of Convolutional Neural Network (CNN) allows for outstanding performance, but at the cost of intensive computation. As significant redundancies inevitably present in such a structure, many works have been proposed to prune the convolutional filters for computation cost reduction. Although ex…

Gradient filters track moving parameters under noisy data and misspecification.

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

A new model optimizes Bloom filters using machine learning.

Develops an inverse particle filter for cognitive systems.

Convolutional neural networks (CNNs) achieve state-of-the-art performance in a wide variety of tasks in computer vision. However, interpreting CNNs still remains a challenge. This is mainly due to the large number of parameters in these networks. Here, we investigate the role of compression and particularly pruning fil…

A novel method reduces dimensionality for filtering SRNs with observed variables.

Kernel learning FBSDE filter improves nonlinear filtering efficiency.

New method filters large networks from financial data to reveal key subnetworks.

Many nonlinear extensions of the Kalman filter, e.g., the extended and the unscented Kalman filter, reduce the state densities to Gaussian densities. This approximation gives sufficient results in many cases. However, this filters only estimate states that are correlated with the observation. Therefore, sequential esti…

Paper proves convergence of Kalman filter on Stiefel manifolds with measurement errors.

Improved Kalman filter for non-linear, non-Gaussian data.

This work analyzes the stability of graph filters under large perturbations.

Advances deep network embedding through multi-filtering GCN.

This work preserves linear invariants in ensemble filters for non-Gaussian data assimilation.

Paper learns to rotate filters for group convolutions.

Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.

EnSF improves accuracy in tracking high-dimensional nonlinear systems.

Non-linear filter aggregation improves image denoising.

Improved Kalman filter for Stiefel manifold measurements.

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

In this paper we introduce a projection method for the space of probability distributions based on the differential geometric approach to statistics. This method is based on a direct L2 metric as opposed to the usual Hellinger distance and the related Fisher Information metric. We explain how this apparatus can be used…

Robust Kalman filtering method for outlier detection.

This paper presents the construction of a particle filter, which incorporates elements inspired by genetic algorithms, in order to achieve accelerated adaptation of the estimated posterior distribution to changes in model parameters. Specifically, the filter is designed for the situation where the subsequent data in on…

Develops inverse unscented Kalman filter for non-linear systems.

New sampling-based approach for filtering problems using multiplicative Gaussian functions.

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

A new filter reduces density fitting to a linear solve, improving performance on nonlinear systems.

In an effort to understand the meaning of the intermediate representations captured by deep networks, recent papers have tried to associate specific semantic concepts to individual neural network filter responses, where interesting correlations are often found, largely by focusing on extremal filter responses. In this …

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

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

Adaptive Heston model calibration using PCRLB and switching filters.

RNF learns distinct representations for Bayesian filtering steps, improving time series prediction accuracy and uncertainty.

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

Filtered conformal ellipsoids for graph-native time series

The paper develops a computational method for efficient online filtering of diffusion processes.

Many sensors, such as range, sonar, radar, GPS and visual devices, produce measurements which are contaminated by outliers. This problem can be addressed by using fat-tailed sensor models, which account for the possibility of outliers. Unfortunately, all estimation algorithms belonging to the family of Gaussian filters…

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

The Kalman filter (KF) is used in a variety of applications for computing the posterior distribution of latent states in a state space model. The model requires a linear relationship between states and observations. Extensions to the Kalman filter have been proposed that incorporate linear approximations to nonlinear m…

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…

Paper presents a robust Kalman filter for state estimation.

We present a filter correlation based model compression approach for deep convolutional neural networks. Our approach iteratively identifies pairs of filters with the largest pairwise correlations and drops one of the filters from each such pair. However, instead of discarding one of the filters from each such pair naï…