arXiv research

GraphSAC detects anomalies in large graphs by sampling and filtering node subsets.

In this paper, we propose a family of graph partition similarity measures that take the topology of the graph into account. These graph-aware measures are alternatives to using set partition similarity measures that are not specifically designed for graph partitions. The two types of measures, graph-aware and set parti…

Inference of space-time varying signals on graphs emerges naturally in a plethora of network science related applications. A frequently encountered challenge pertains to reconstructing such dynamic processes, given their values over a subset of vertices and time instants. The present paper develops a graph-aware kernel…

GCAN detects fake news on social media with explanations.

To address the sparsity and cold start problem of collaborative filtering, researchers usually make use of side information, such as social networks or item attributes, to improve recommendation performance. This paper considers the knowledge graph as the source of side information. To address the limitations of existi…

Functional connections in the brain are frequently represented by weighted networks, with nodes representing locations in the brain, and edges representing the strength of connectivity between these locations. One challenge in analyzing such data is that inference at the individual edge level is not particularly biolog…

A scalable GP model for online uncertainty quantification over graphs.

Transformer adapts to graphs with adaptive attention and auto-regressive decoding.

We introduce novel communication strategies in synchronous distributed Deep Learning consisting of decentralized gradient reduction orchestration and computational graph-aware grouping of gradient tensors. These new techniques produce an optimal overlap between computation and communication and result in near-linear sc…

We consider stochastic multi-armed bandit problems with graph feedback, where the decision maker is allowed to observe the neighboring actions of the chosen action. We allow the graph structure to vary with time and consider both deterministic and Erdős-Rényi random graph models. For such a graph feedback model, we fir…

This paper focuses on spectral filters on graphs, namely filters defined as elementwise multiplication in the frequency domain of a graph. In many graph signal processing settings, it is important to transfer a filter from one graph to another. One example is in graph convolutional neural networks (ConvNets), where the…

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.

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.

Convolutional Bayesian filtering generalizes state estimation by incorporating inequality conditions.

EnSF improves accuracy in tracking high-dimensional nonlinear systems.

Improved Kalman filter for Stiefel manifold measurements.

Sheaf Neural Networks improve graph learning with geometric insights.

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.

Recent work has suggested enhancing Bloom filters by using a pre-filter, based on applying machine learning to determine a function that models the data set the Bloom filter is meant to represent. Here we model such learned Bloom filters,, with the following outcomes: (1) we clarify what guarantees can and cannot be as…

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.

Unified framework for multi-user bandits using Laplacian kernels.

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.

Triangular, overlapping Mel-scaled filters ("f-banks") are the current standard input for acoustic models that exploit their input's time-frequency geometry, because they provide a psycho-acoustically motivated time-frequency geometry for a speech signal. F-bank coefficients are provably robust to small deformations in…

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

Adaptive Heston model calibration using PCRLB and switching filters.

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

Filtered conformal ellipsoids for graph-native time series