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.
Wavelets help compress neural networks efficiently.
Paper introduces rational Gaussian wavelets for efficient signal approximation.
MODWST improves classification tasks with wavelet scattering.
In this work we propose a method for learning wavelet filters directly from data. We accomplish this by framing the discrete wavelet transform as a modified convolutional neural network. We introduce an autoencoder wavelet transform network that is trained using gradient descent. We show that the model is capable of le…
VDWs enhance graph neural networks for analyzing complex data.
The wavelet transform has seen success when incorporated into neural network architectures, such as in wavelet scattering networks. More recently, it has been shown that the dual-tree complex wavelet transform can provide better representations than the standard transform. With this in mind, we extend our previous meth…
New method selects diffusion scales for graph wavelets.
Wavelet Kolmogorov-Arnold Networks improve federated learning performance.
This paper proposes the use of wavelet methods to estimate U.S. core inflation. It explains wavelet methods and suggests they are ideally suited to this task. Comparisons are made with traditional CPI-based and regression-based measures for their performance in following trend inflation and predicting future inflation.…
Wavelet Networks learn from raw time-series data, outperforming conventional CNNs.
Revisits SWIFT method for option pricing using Shannon wavelets.
Optimizes wavelets for graph classification using spectral wavelet signatures and persistence diagrams.
Improved texture synthesis using wavelet-based statistics with rectifier non-linearity.
Cake wavelets minimize orientation score uncertainty.
We present graph wavelet neural network (GWNN), a novel graph convolutional neural network (CNN), leveraging graph wavelet transform to address the shortcomings of previous spectral graph CNN methods that depend on graph Fourier transform. Different from graph Fourier transform, graph wavelet transform can be obtained …
Wavelet SGM accelerates generative modeling with linear time complexity.
MLShrink integrates machine learning with wavelet shrinkage for denoising.
Wavelet scattering spectra model non-Gaussian time-series, proving scale invariance for self-similar processes.
Unified method for simultaneous denoising and clustering.
Variational Autoencoders (VAE) are probabilistic deep generative models underpinned by elegant theory, stable training processes, and meaningful manifold representations. However, they produce blurry images due to a lack of explicit emphasis over high-frequency textural details of the images, and the difficulty to dire…
AWD distills neural network info into interpretable wavelets.
A new WNN framework selects wavelet bases for efficient learning.
Pixel intensity is a widely used feature for clustering and segmentation algorithms, the resulting segmentation using only intensity values might suffer from noises and lack of spatial context information. Wavelet transform is often used for image denoising and classification. We proposed a novel method to incorporate …
WaveletGAN improves GANs by homogenizing noise through multi-channel wavelet filtering.
In this paper we propose and study a family of continuous wavelets on general domains, and a corresponding stochastic discretization that we call Monte Carlo wavelets. First, using tools from the theory of reproducing kernel Hilbert spaces and associated integral operators, we define a family of continuous wavelets by …
Wav-KAN improves neural network interpretability and performance.
Volatility dynamics of wavelet - filtered stock price time series is studied. Using the universal thresholding method of wavelet filtering and a principle of minimal linear autocorrelation of noise component we find that the quantitative characteristics of volatility dynamics of denoised series are noticeably different…
Wavelet analysis reveals non-linear dynamics in cryptocurrency prices.
New method estimates and samples high-dimensional probability distributions avoiding optimization and approximation curse.
Wavelet analysis reveals financialization effects on oil-food price correlation.
WCAM assesses neural network reliability by attributing decisions to wavelet scales.
Wavelets model complex interactions in spatial transcriptomics.
In this paper we introduce a significant improvement to the popular tree-based Stochastic Gradient Boosting algorithm using a wavelet decomposition of the trees. This approach is based on harmonic analysis and approximation theoretical elements, and as we show through extensive experimentation, our wavelet based method…
Develops wavelet-based neural network approximation theory.
A general machine learning architecture is introduced that uses wavelet scattering coefficients of an inputted three dimensional signal as features. Solid harmonic wavelet scattering transforms of three dimensional signals were previously introduced in a machine learning framework for the regression of properties of sm…
We perform wavelet decomposition of high frequency financial time series into large and small time scale components. Taking the FTSE100 index as a case study, and working with the Haar basis, it turns out that the small scale component defined by most ( 99.6%) of the wavelet coefficients can be neglected for th…
The assessment of co-movement among metals is crucial to better understand the behaviors of the metal prices and the interactions with others that affect the changes in prices. In this study, both Wavelet Analysis and VARMA (Vector Autoregressive Moving Average) models are utilized. First, Multiple Wavelet Coherence (M…
Improved signal classification using multiple wavelets and their smooth coefficients.
Alternative wavelet analysis method for financial signals.
In this paper we studied about the wavelet identification of the thresholds and time delay for more general case without the constraint that the time delay is smaller than the order of the model. Here we composed an empirical wavelet from the SETAR (Self-Exciting Threshold Autoregressive) model and identified the thres…
In the present paper, a fuzzy logic based method is combined with wavelet decomposition to develop a step-by-step dynamic hybrid model for the estimation of financial time series. Empirical tests on fuzzy regression, wavelet decomposition as well as the new hybrid model are conducted on the well known index fin…
We introduce multiscale invariant dictionaries to estimate quantum chemical energies of organic molecules, from training databases. Molecular energies are invariant to isometric atomic displacements, and are Lipschitz continuous to molecular deformations. Similarly to density functional theory (DFT), the molecule is re…
Paper proposes integrating wavelet transform, channel attention, and LSTM for better stock price prediction.
A cost-effective method to generate high-resolution images using wavelet-based super-resolution.
New wavelet frames constructed from reproducing kernels for continuous and discrete domains.
We propose a numerical algorithm for backward stochastic differential equations based on time discretization and trigonometric wavelets. This method combines the effectiveness of Fourier-based methods and the simplicity of a wavelet-based formula, resulting in an algorithm that is both accurate and easy to implement. F…
Deep neural networks have presented impressive performance in biometric applications. However, their performance is highly at risk when facing carefully crafted input samples known as adversarial examples. In this paper, we present three defense strategies to detect adversarial iris examples. These defense strategies a…
The note evaluates different methods for option pricing using Shannon Wavelets.