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.
Financial frequency combs emerge from macroeconomic long-range memory.
Paper predicts VQ performance for LSF using DMM in the ΔLSF domain.
In this paper, we address the fundamental problem of line spectral estimation in a Bayesian framework. We target model order and parameter estimation via variational inference in a probabilistic model in which the frequencies are continuous-valued, i.e., not restricted to a grid; and the coefficients are governed by a …
Deep neural networks are biased towards low frequencies, affecting global behavior.
This paper is concerned about sparse, continuous frequency estimation in line spectral estimation, and focused on developing gridless sparse methods which overcome grid mismatches and correspond to limiting scenarios of existing grid-based approaches, e.g., optimization and SPICE, with an infinitely dense grid…
FFN addresses spectral bias in neural value approximation, improving reinforcement learning performance.
We study the properties of memory of a financial time series adopting two different methods of analysis, the detrended fluctuation analysis (DFA) and the analysis of the power spectrum (PSA). The methods are applied on three time series: one of high-frequency returns, one of shuffled returns and one of absolute values …
HyFAD improves time series imputation by combining time and frequency diffusion.
FreDN separates trends and periodicities in non-stationary time series forecasts.
Study shows non-spectrality of certain curves and line segments.
Bayesian model reconstructs time and frequency data robustly.
SARGAN uses GANs to fill in missing radar frequency bands.
High-dimensional inference for sparse spectral precision matrices
Study cryptocurrency price dynamics using adaptive EMD and spectral analysis.
An important step in speaker verification is extracting features that best characterize the speaker voice. This paper investigates a front-end processing that aims at improving the performance of speaker verification based on the SVMs classifier, in text independent mode. This approach combines features based on conven…
We propose a new framework for manifold denoising based on processing in the graph Fourier frequency domain, derived from the spectral decomposition of the discrete graph Laplacian. Our approach uses the Spectral Graph Wavelet transform in order to per- form non-iterative denoising directly in the graph frequency domai…
Many spectral unmixing methods rely on the non-negative decomposition of spectral data onto a dictionary of spectral templates. In particular, state-of-the-art music transcription systems decompose the spectrogram of the input signal onto a dictionary of representative note spectra. The typical measures of fit used to …
New method for spectral and Bergman kernels under local spectral gap condition.
Previous research has shown that computation of convolution in the frequency domain provides a significant speedup versus traditional convolution network implementations. However, this performance increase comes at the expense of repeatedly computing the transform and its inverse in order to apply other network operati…
Deep learning model estimates multiple f0s, melodies, vocals, and bass lines from music.
Quantile-Frequency Analysis detects nonlinear dynamics in financial time series.
Derives spectral density function for symplectic manifolds.
Let (M, g) be a compact smooth Riemannian manifold. We obtain new off-diagonal estimates as λ tend to infinity for the remainder in the pointwise Weyl Law for the kernel of the spectral projector of the Laplacian onto functions with frequency at most λ. A corollary is that, when rescaled around a non self-focal point, …
High-frequency financial data of the foreign exchange market (EUR/CHF, EUR/GBP, EUR/JPY, EUR/NOK, EUR/SEK, EUR/USD, NZD/USD, USD/CAD, USD/CHF, USD/JPY, USD/NOK, and USD/SEK) are analyzed by utilizing the Kullback-Leibler divergence between two normalized spectrograms of the tick frequency and the generalized Jensen-Sha…
Empirical analysis of the foreign exchange market is conducted based on methods to quantify similarities among multi-dimensional time series with spectral distances introduced in [A.-H. Sato, Physica A, 382 (2007) 258--270]. As a result it is found that the similarities among currency pairs fluctuate with the rotation …
In this work a spectral theory for 2-dimensional, simply periodic, complex-valued solutions u of the sinh-Gordon equation is developed. Spectral data for such solutions are defined (following Hitchin and Bobenko) and the space of spectral data is described by an asymptotic characterization. Using methods of asymptotic …
SPGD improves adversarial training efficiency and accuracy.
Framework clusters noisy MTS with robust fuzzy clustering, improving accuracy over existing methods.
Spectral Adaptive Conformal Prediction for Structured Non-Exchangeable Data
A robust method for decomposing spectral peaks robust to distortion and interference.
The discrete Nahm equations, a system of matrix valued difference equations, arose in the work of Braam and Austin on half-integral mass hyperbolic monopoles. We show that the discrete Nahm equations are completely integrable in a natural sense: to any solution we can associate a spectral curve and a holomorphic line-b…
We analyze the frequency spectrum of quantum neural networks using algebraic methods and prove maximality results.
We associate a Taylor tower supplied by calculus of the embedding functor to the space of long knots and study its cohomology spectral sequence. The combinatorics of the spectral sequence along the line of total degree zero leads to chord diagrams with relations as in finite type knot theory. We show that the spectral …
For a symplectic manifold with quantizing line bundle, a choice of almost complex structure determines a Laplacian acting on tensor powers of the bundle. For high tensor powers Guillemin-Uribe showed that there is a well-defined cluster of low-lying eigenvalues, whose distribution is described by a spectral density fun…
We define a pseudo-inverse for line graphs using linear integer programming.
Deep neural networks can generalize by reducing high-frequency noise over time, not always following a monotonic learning bias.
Graph signal processing detects hallucinations in large language models.
We analyze DMs using spectral methods to design effective noise schedules.
Developed a diffusion model on spherical data, addressing geometric and stochastic challenges.
Recent research in off-the-grid compressed sensing (CS) has demonstrated that, under certain conditions, one can successfully recover a spectrally sparse signal from a few time-domain samples even though the dictionary is continuous. In particular, atomic norm minimization was proposed in \cite{tang2012csotg} to recove…
A simple analytically solvable model exhibiting a 1/f spectrum in an arbitrarily wide frequency range was recently proposed by Kaulakys and Meskauskas (KM). Signals consisting of a sequence of pulses show that inherent origin of the 1/f noise is Brownian fluctuations of the average intervent time between subsequent pul…
In this paper we study the asymptotic behaviour of the spectral function corresponding to the lower part of the spectrum of the Kodaira Laplacian on high tensor powers of a holomorphic line bundle. This implies a full asymptotic expansion of this function on the set where the curvature of the line bundle is non-degener…
Proposes a deep spectral Q-learning for mobile health data.
Study on Matérn covariance approximations on grids, finding issues with high-frequency aliasing.
A deep learning framework learns wavelet packet transforms for efficient feature extraction.
Spectral Independence Criterion helps infer cause-effect relationships in time series.
Detects corruption in agentic models during execution.
This work analyzes how different layers in deep neural networks contribute to generalization error.