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.
GSAN learns adaptive node representations using geometric scattering and attention.
We introduce general scattering transforms as mathematical models of deep neural networks with l2 pooling. Scattering networks iteratively apply complex valued unitary operators, and the pooling is performed by a complex modulus. An expected scattering defines a contractive representation of a high-dimensional probabil…
Scattering networks maximize separation on low-dimensional data.
The scattering transform is a multilayered wavelet-based deep learning architecture that acts as a model of convolutional neural networks. Recently, several works have introduced generalizations of the scattering transform for non-Euclidean settings such as graphs. Our work builds upon these constructions by introducin…
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of convolutional neural networks. Inspired by recent interest in geometric deep learning, which aims to generalize convolutional neural networks to manifold and graph-structured domains, we define a geometric…
Scattering transforms are non-trainable deep convolutional architectures that exploit the multi-scale resolution of a wavelet filter bank to obtain an appropriate representation of data. More importantly, they are proven invariant to translations, and stable to perturbations that are close to translations. This stabili…
Scattering networks are a class of designed Convolutional Neural Networks (CNNs) with fixed weights. We argue they can serve as generic representations for modelling images. In particular, by working in scattering space, we achieve competitive results both for supervised and unsupervised learning tasks, while making pr…
WideBNet learns inverse scattering from wide-band data efficiently and stably.
Deep learning solves wave-based inverse problems, including super-resolution imaging.
Scattering GCN improves graph neural networks by filtering oversmoothing.
The Euclidean scattering transform was introduced nearly a decade ago to improve the mathematical understanding of the success of convolutional neural networks (ConvNets) in image data analysis and other tasks. Inspired by recent interest in geometric deep learning, which aims to generalize ConvNets to manifold and gra…
Enhances CNN feature extractors' separation capacity analysis.
MODWST improves classification tasks with wavelet scattering.
Enhances DSN with multi-family wavelet transforms and sparsity.
Deep neural networks correct Mie scattering in FTIR spectra of biological samples.
A new GNN module learns geometric scattering features for better graph classification and feature exploration.
Unified geometric scattering model for measure spaces.
New MHSNs extract multiscale features from complex data for robust classification.
Generative Adversarial Nets (GANs) and Variational Auto-Encoders (VAEs) provide impressive image generations from Gaussian white noise, but the underlying mathematics are not well understood. We compute deep convolutional network generators by inverting a fixed embedding operator. Therefore, they do not require to be o…
We introduce a sparse scattering deep convolutional neural network, which provides a simple model to analyze properties of deep representation learning for classification. Learning a single dictionary matrix with a classifier yields a higher classification accuracy than AlexNet over the ImageNet 2012 dataset. The netwo…
New deep learning model estimates scattering timescale of FRBs efficiently.
FAT-GAN simulates electron-proton scattering without theoretical assumptions.
A new hybrid GNN framework tackles oversmoothing in graph data.
A scattering transform defines a signal representation which is invariant to translations and Lipschitz continuous relatively to deformations. It is implemented with a non-linear convolution network that iterates over wavelet and modulus operators. Lipschitz continuity locally linearizes deformations. Complex classes o…
In this article we propose building general-purpose function approximators on top of Haar Scattering Networks. We advocate that this architecture enables a better comprehension of feature extraction, in addition to its implementation simplicity and low computational costs. We show its approximation and feature extracti…
New methods implement manifold scattering transform for high-dimensional point cloud data.
We explore the generalization of scattering transforms from traditional (e.g., image or audio) signals to graph data, analogous to the generalization of ConvNets in geometric deep learning, and the utility of extracted graph features in graph data analysis. In particular, we focus on the capacity of these features to r…
Robust scatter estimation is a fundamental task in statistics. The recent discovery on the connection between robust estimation and generative adversarial nets (GANs) by Gao et al. (2018) suggests that it is possible to compute depth-like robust estimators using similar techniques that optimize GANs. In this paper, we …
Motivated by the prediction of cell loads in cellular networks, we formulate the following new, fundamental problem of statistical learning of geometric marks of point processes: An unknown marking function, depending on the geometry of point patterns, produces characteristics (marks) of the points. One aims at learnin…
In vivo diffusion tensor imaging (DTI) is a promising technique to investigate noninvasively the fiber structures of the in vivo human heart. However, signal loss due to motions remains a persistent problem in in vivo cardiac DTI. We propose a novel motion-compensation method for investigating in vivo myocardium struct…
Model predicts stable molecules with AI and physics constraints.
We introduce scattering-symplectic manifolds, manifolds with a type of minimally degenerate Poisson structure that is not too restrictive so as to have a large class of examples, yet restrictive enough for standard Poisson invariants to be computable. This paper will demonstrate the potential of the scattering symplect…
Paper explains scattering diagrams' role in mirror symmetry.
In this paper we propose a scalable version of a state-of-the-art deterministic time-invariant feature extraction approach based on consecutive changes of basis and nonlinearities, namely, the scattering network. The first focus of the paper is to extend the scattering network to allow the use of higher order nonlinear…
New method uses broken scattering to uniquely identify Finsler manifolds.
New method learns soliton dynamics from scattering data without assuming known equations.
Scattering representations simplify SBI for images without extra compression.
The paper establishes scattering theory for wave equations on Schwarzschild spacetime.
Paper develops formulas for shape derivatives in wave scattering.
Study scattering rigidity on stationary manifolds using geodesics.
Study on scattering geodesics on modular surface and their sojourn times.
Physics-informed neural networks improve baryonic predictions from dark matter simulations.
Scattering theory on Riemann surfaces with explicit matrix and generalized period mappings.
Wavelet scattering predicts material properties beyond training data.
Study on recovering Lorentzian metrics from scattering data.
Bayesian Scattering offers a simple baseline for image data uncertainty.
Scattering theory for linearised gravity on Schwarzschild black hole exterior.
We construct continuous families of scattering manifolds with the same scattering phase. The manifolds are compactly supported metric perturbations of Euclidean for . The metric perturbation may have arbitrarily small support.