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 method for testing directed graphs using surrogate data.
Novel Haar-Laplacian for directed graphs enhances spectral graph applications.
Paper reviews multi-way graph signal processing for tensor data.
Modern data introduces new challenges to classic signal processing approaches, leading to a growing interest in the field of graph signal processing. A powerful and well established model for real world signals in various domains is sparse representation over a dictionary, combined with the ability to train the diction…
This paper reviews zeroth-order optimization in signal processing and machine learning.
Gaussian processes help in modeling complex, nonlinear relationships in signal processing.
HR-calculus enables adaptive processing of quaternion signals.
This tutorial covers methods for handling missing data in SP and ML.
New method clusters directed graphs using Koopman operators.
Paper improves DOA estimation in sparse arrays using Siamese neural networks.
We describe ways to define and calculate -norm signal subspaces which are less sensitive to outlying data than -calculated subspaces. We focus on the computation of the maximum-projection principal component of a data matrix containing N signal samples of dimension D and conclude that the general proble…
Proposes TSBP for matching topological signal distributions.
Goal: This paper deals with the problems that some EEG signals have no good sparse representation and single channel processing is not computationally efficient in compressed sensing of multi-channel EEG signals. Methods: An optimization model with L0 norm and Schatten-0 norm is proposed to enforce cosparsity and low r…
The paper introduces novel Gaussian process models for vector-valued signals on manifolds.
A novel Gaussian process approach for deconvolution of missing data signals.
Most generative models of audio directly generate samples in one of two domains: time or frequency. While sufficient to express any signal, these representations are inefficient, as they do not utilize existing knowledge of how sound is generated and perceived. A third approach (vocoders/synthesizers) successfully inco…
Paper uses relaxation techniques to find optimal brokerage fees with private signals.
Machine learning algorithms typically rely on optimization subroutines and are well-known to provide very effective outcomes for many types of problems. Here, we flip the reliance and ask the reverse question: can machine learning algorithms lead to more effective outcomes for optimization problems? Our goal is to trai…
Human infants can discover words directly from unsegmented speech signals without any explicitly labeled data. In this paper, we develop a novel machine learning method called nonparametric Bayesian double articulation analyzer (NPB-DAA) that can directly acquire language and acoustic models from observed continuous sp…
New method for separating mixed signals with nonlinear functions.
Direction of arrival (DOA) estimation is a classical problem in signal processing with many practical applications. Its research has recently been advanced owing to the development of methods based on sparse signal reconstruction. While these methods have shown advantages over conventional ones, there are still difficu…
Survey of complex-valued neural networks for improved performance.
A framework for decentralized optimization using first-order methods.
Non-negative blind source separation (non-negative BSS), which is also referred to as non-negative matrix factorization (NMF), is a very active field in domains as different as astrophysics, audio processing or biomedical signal processing. In this context, the efficient retrieval of the sources requires the use of sig…
New methods learn DAGs from noisy data, adapting to noise levels.
New algorithms detect and estimate rank-one signals with prior directional information.
In this paper, we consider solving a class of nonconvex and nonsmooth problems frequently appearing in signal processing and machine learning research. The traditional alternating direction method of multipliers encounters troubles in both mathematics and computations in solving the nonconvex and nonsmooth subproblem. …
One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions,…
This paper introduces a new sparse spatio-temporal structured Gaussian process regression framework for online and offline Bayesian inference. This is the first framework that gives a time-evolving representation of the interdependencies between the components of the sparse signal of interest. A hierarchical Gaussian p…
Survey of tractable nonconvex problems using symmetry.
Many signals on Cartesian product graphs appear in the real world, such as digital images, sensor observation time series, and movie ratings on Netflix. These signals are "multi-dimensional" and have directional characteristics along each factor graph. However, the existing graph Fourier transform does not distinguish …
The paper shows that causal identification is not essential for efficient portfolios, focusing on geometric sufficiency conditions.
Tutorials on signal processing on higher-order networks like simplicial complexes and hypergraphs.
Dictionary learning is a cutting-edge area in imaging processing, that has recently led to state-of-the-art results in many signal processing tasks. The idea is to conduct a linear decomposition of a signal using a few atoms of a learned and usually over-completed dictionary instead of a pre-defined basis. Determining …
Privacy subsidy found in market trading with noisy direction signals.
LogSpecT learns graphs from stationary signals without infeasibility issues.
Paper introduces signal processing on cell complexes.
Background: Fluctuating hearing loss is characteristic of Meniere's Disease (MD) during acute episodes. However, no reliable audiometric hallmarks are available for counselling the hearing recovery possibility. Aims/Objectives: To find parameters for predicting MD hearing outcomes. Material and Methods: We applied mach…
Algorithm learns graph ARMA processes for missing signal estimation.
Reinforcement Learning (RL) agents require the specification of a reward signal for learning behaviours. However, introduction of corrupt or stochastic rewards can yield high variance in learning. Such corruption may be a direct result of goal misspecification, randomness in the reward signal, or correlation of the rew…
Signal retrieval from a series of indirect measurements is a common task in many imaging, metrology and characterization platforms in science and engineering. Because most of the indirect measurement processes are well-described by physical models, signal retrieval can be solved with an iterative optimization that enfo…
We propose a data-driven algorithm for the maximum a posteriori (MAP) estimation of stochastic processes from noisy observations. The primary statistical properties of the sought signal is specified by the penalty function (i.e., negative logarithm of the prior probability density function). Our alternating direction m…
Proposes a new complex Gaussian distribution for better modeling of complex-valued signals.
Deep Gaussian Processes model functions on DAGs with partially observed data.
Proposes LSGP for better graph signal representation.
A new method for analyzing high-dimensional time-series data using deep neural networks.
Paper reveals hidden convexities in deep learning models using sparse signal processing.
New algorithms improve signal processing in federated learning.