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 algorithms improve signal processing in federated learning.
The paper focuses on the sparse approximation of signals using overcomplete representations, such that it preserves the (prior) structure of multi-dimensional signals. The underlying optimization problem is tackled using a multi-dimensional split Bregman optimization approach. An extensive empirical evaluation shows ho…
We present the group fused Lasso for detection of multiple change-points shared by a set of co-occurring one-dimensional signals. Change-points are detected by approximating the original signals with a constraint on the multidimensional total variation, leading to piecewise-constant approximations. Fast algorithms are …
Approximate Message Passing (AMP) has been shown to be an excellent statistical approach to signal inference and compressed sensing problem. The AMP framework provides modularity in the choice of signal prior; here we propose a hierarchical form of the Gauss-Bernouilli prior which utilizes a Restricted Boltzmann Machin…
Sparse coding in learned dictionaries has been established as a successful approach for signal denoising, source separation and solving inverse problems in general. A dictionary learning method adapts an initial dictionary to a particular signal class by iteratively computing an approximate factorization of a training …
This paper improves support recovery in universal one-bit compressed sensing.
For node level graph encoding, a recent important state-of-art method is the graph convolutional networks (GCN), which nicely integrate local vertex features and graph topology in the spectral domain. However, current studies suffer from several drawbacks: (1) graph CNNs relies on Chebyshev polynomial approximation whi…
Study uses random matrix theory to improve tensor approximation accuracy.
SRMD uses random features for efficient time-frequency analysis.
New algorithms improve rank one signal estimation from noisy data.
Paper explores supervised learning methods to approximate ideal observer for joint signal detection and localization.
This paper presents a novel approach for approximate integration over the uncertainty of noise and signal variances in Gaussian process (GP) regression. Our efficient and straightforward approach can also be applied to integration over input dependent noise variance (heteroscedasticity) and input dependent signal varia…
New algorithm improves signal reconstruction from noisy measurements with side information.
It is widely accepted that optimization of medical imaging system performance should be guided by task-based measures of image quality (IQ). Task-based measures of IQ quantify the ability of an observer to perform a specific task such as detection or estimation of a signal (e.g., a tumor). For binary signal detection t…
Receiver algorithms which combine belief propagation (BP) with the mean field (MF) approximation are well-suited for inference of both continuous and discrete random variables. In wireless scenarios involving detection of multiple signals, the standard construction of the combined BP-MF framework includes the equalizat…
Combines neural networks with splitting-up method for filtering equations.
Paper introduces rational Gaussian wavelets for efficient signal approximation.
This paper improves support recovery in universal one-bit compressed sensing with fewer measurements.
Paper presents a unique method to recover signals from their bispectrum.
New AMP algorithm estimates signals and latent variables in mixed regression models.
Auto-Encoders are unsupervised models that aim to learn patterns from observed data by minimizing a reconstruction cost. The useful representations learned are often found to be sparse and distributed. On the other hand, compressed sensing and sparse coding assume a data generating process, where the observed data is g…
The Ideal Observer (IO) performance has been advocated when optimizing medical imaging systems for signal detection tasks. However, analytical computation of the IO test statistic is generally intractable. To approximate the IO test statistic, sampling-based methods that employ Markov-Chain Monte Carlo (MCMC) technique…
New algorithm for signal estimation in noisy matrix models.
Paper develops a new algorithm for sparse signal recovery.
Generalizes PCA and ICA for continuous-time signals using neural networks.
Sampling is a fundamental topic in graph signal processing, having found applications in estimation, clustering, and video compression. In contrast to traditional signal processing, the irregularity of the signal domain makes selecting a sampling set non-trivial and hard to analyze. Indeed, though conditions for graph …
Speech produced by human vocal apparatus conveys substantial non-semantic information including the gender of the speaker, voice quality, affective state, abnormalities in the vocal apparatus etc. Such information is attributed to the properties of the voice source signal, which is usually estimated from the speech sig…
New method for separating mixed signals with nonlinear functions.
In this work, we consider compressed sensing reconstruction from measurements of -sparse structured signals which do not possess a writable correlation model. Assuming that a generative statistical model, such as a Boltzmann machine, can be trained in an unsupervised manner on example signals, we demonstrate how…
Recent research has shown that performance in signal processing tasks can often be significantly improved by using signal models based on sparse representations, where a signal is approximated using a small number of elements from a fixed dictionary. Unfortunately, inference in this model involves solving non-smooth op…
The paper describes a method to infer the signal-to-noise ratio in portfolio optimization.
Bandlimited random neural networks may not approximate all functions perfectly.
The paper proposes a least squares method for binary compressive sampling with low intrinsic dimension signals.
Paper presents a simple method for accurate user localization in urban areas.
Paper proposes AMP with spectral initialization for robust signal estimation.
This paper reconstructs complex graph signals using kernel methods on manifolds.
Deep clustering is a recently introduced deep learning architecture that uses discriminatively trained embeddings as the basis for clustering. It was recently applied to spectrogram segmentation, resulting in impressive results on speaker-independent multi-speaker separation. In this paper we extend the baseline system…
New AMP algorithms for rotationally invariant models with reduced complexity.
We study signal recovery on graphs based on two sampling strategies: random sampling and experimentally designed sampling. We propose a new class of smooth graph signals, called approximately bandlimited, which generalizes the bandlimited class and is similar to the globally smooth class. We then propose two recovery s…
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…
Overview of non-stochastic-gradient SA algorithms in signal processing and ML.
We propose a learning-based approach for estimating the spectrum of a multisinusoidal signal from a finite number of samples. A neural-network is trained to approximate the spectra of such signals on simulated data. The proposed methodology is very flexible: adapting to different signal and noise models only requires m…
We focus in this work on the estimation of the first eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need such signals to be able to exactly recover as many of the smallest eigenvectors, regardless of the number of nodes in the graph. In addition, we address…
Paper improves signal proportion estimation by accounting for variable dependence.
Study on tensor signal estimation from incomplete data.
New algorithm improves signal recovery from noisy measurements with theoretical guarantees.
Bayesian method improves EEG source localization and estimates skull conductivity.
New metrics compare rational spectra using optimal transport.