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.
New sampling-based approach for filtering problems using multiplicative Gaussian functions.
Novel filter uses deep BSDE for nonlinear density approximation.
Paper uses averaging from many particle filters to approximate posterior predictive distributions.
This study uses neural networks to approximate Bayesian filtering problems.
Transformers can approximate Kalman Filtering in linear systems with small error.
A new numerical scheme approximates nonlinear filtering densities for noisy and partial measurements.
A new method improves Bayesian filtering in nonlinear systems.
A new method approximates deep neural networks using Kalman Filters.
A new method for Gaussian filtering using gradient flows and Wasserstein metrics.
We formulate probabilistic numerical approximations to solutions of ordinary differential equations (ODEs) as problems in Gaussian process (GP) regression with non-linear measurement functions. This is achieved by defining the measurement sequence to consist of the observations of the difference between the derivative …
Deep density methods improve filtering in high-dimensional systems.
A new method reduces high-dimensional filtering to quadratic complexity.
Combines neural networks with splitting-up method for filtering equations.
We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling …
Develops optimal low-dimensional approximations to high-dimensional SDEs.
New filters for non-linear systems achieve closed-form solutions.
FLUID uses flows to unify filtering and smoothing for complex systems.
A deep learning method solves nonlinear filtering problems efficiently.
The process of dynamic state estimation (filtering) based on point process observations is in general intractable. Numerical sampling techniques are often practically useful, but lead to limited conceptual insight about optimal encoding/decoding strategies, which are of significant relevance to Computational Neuroscien…
We use GANs and signatures to approximate conditional laws in filtering and prediction of diffusion processes.
Develops an inverse particle filter for cognitive systems.
The unscented transformation (UT) is an efficient method to solve the state estimation problem for a non-linear dynamic system, utilizing a derivative-free higher-order approximation by approximating a Gaussian distribution rather than approximating a non-linear function. Applying the UT to a Kalman filter type estimat…
EnSF improves accuracy in tracking high-dimensional nonlinear systems.
We seek to learn an effective policy for a Markov Decision Process (MDP) with continuous states via Q-Learning. Given a set of basis functions over state action pairs we search for a corresponding set of linear weights that minimizes the mean Bellman residual. Our algorithm uses a Kalman filter model to estimate those …
We revisit the development of grid based recursive approximate filtering of general Markov processes in discrete time, partially observed in conditionally Gaussian noise. The grid based filters considered rely on two types of state quantization: The \textit{Markovian} type and the \textit{marginal} type. We propose a s…
Enhanced ensemble filters use machine learning to improve accuracy in filtering models.
Transformers can solve complex filtering problems for non-Gaussian signals.
New deep learning method approximates Benes filter model.
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 …
We introduce a probabilistic approach to the LMS filter. By means of an efficient approximation, this approach provides an adaptable step-size LMS algorithm together with a measure of uncertainty about the estimation. In addition, the proposed approximation preserves the linear complexity of the standard LMS. Numerical…
It is not easy to design and run Convolutional Neural Networks (CNNs) due to: 1) finding the optimal number of filters (i.e., the width) at each layer is tricky, given an architecture; and 2) the computational intensity of CNNs impedes the deployment on computationally limited devices. Oracle Pruning is designed to rem…
Kernel learning FBSDE filter improves nonlinear filtering efficiency.
A new ensemble filter uses transport maps and MMD optimization for high-dimensional data assimilation.
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…
The decentralized particle filter (DPF) was proposed recently to increase the level of parallelism of particle filtering. Given a decomposition of the state space into two nested sets of variables, the DPF uses a particle filter to sample the first set and then conditions on this sample to generate a set of samples for…
We consider a Hidden Markov Model (HMM) where the integrated continuous-time Markov chain can be observed at discrete time points perturbed by a Brownian motion. The aim is to derive a filter for the underlying continuous-time Markov chain. The recursion formula for the discrete-time filter is easy to derive, however i…
New method improves Kalman filtering and smoothing for large state spaces.
Collaborative filtering (CF) is a popular technique in today's recommender systems, and matrix approximation-based CF methods have achieved great success in both rating prediction and top-N recommendation tasks. However, real-world user-item rating matrices are typically sparse, incomplete and noisy, which introduce ch…
The paper develops ML algorithms for calibrating credit rating transition models for high and low default portfolios.
In order to interact intelligently with objects in the world, animals must first transform neural population responses into estimates of the dynamic, unknown stimuli which caused them. The Bayesian solution to this problem is known as a Bayes filter, which applies Bayes' rule to combine population responses with the pr…
A data filtering method for cluster analysis is proposed, based on minimizing a least squares function with a weighted -norm penalty. To overcome the discontinuity of the objective function, smooth non-convex functions are employed to approximate the -norm. The convergence of the global minimum points o…
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…
ATPF combines PF and EnKF for better inference in complex systems.
Proposes CE-BASS for robust Kalman filtering with innovative and additive outliers.
New method improves nonlinear filtering accuracy with reduced computation.
Dropout neural networks can approximate any function with high probability.
HKF uses neural networks to adapt Kalman filters for dynamic channel tracking.
Kernel-based Bayesian filter for nonlinear systems using infinite-dimensional operators.