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.
Band-limited training reduces resource usage without sacrificing accuracy.
The paper improves confidence regions for band-limited functions using tighter norm bounds and majority voting.
The paper creates nonparametric confidence bands for band-limited functions.
We study the volume distribution of nodal domains of random band-limited functions on generic manifolds, and find that in the high energy limit a typical instance obeys a deterministic universal law, independent of the manifold. Some of the basic qualitative properties of this law, such as its support, monotonicity and…
Let be a smooth compact Riemannian surface with no boundary. Given a smooth vector field with finitely many zeroes on , we study the distribution of the number of tangencies to of the nodal components of random band-limited functions. It is determined that in the high-energy limit, these obey a unive…
In this paper, a nonparametric maximum likelihood (ML) estimator for band-limited (BL) probability density functions (pdfs) is proposed. The BLML estimator is consistent and computationally efficient. To compute the BLML estimator, three approximate algorithms are presented: a binary quadratic programming (BQP) algorit…
Wiatowski and Bölcskei, 2015, proved that deformation stability and vertical translation invariance of deep convolutional neural network-based feature extractors are guaranteed by the network structure per se rather than the specific convolution kernels and non-linearities. While the translation invariance result appli…
This work uses sampling theory to analyze smoothness and error bounds of finite neural networks.
The paper improves nonparametric confidence bands for band-limited functions.
We propose a novel class of Gaussian processes (GPs) whose spectra have compact support, meaning that their sample trajectories are almost-surely band limited. As a complement to the growing literature on spectral design of covariance kernels, the core of our proposal is to model power spectral densities through a rect…
Band-limited SAC improves learning efficiency and stability in simulated environments.
The class of non-rigid registration methods proposed in the framework of PDE-constrained Large Deformation Diffeomorphic Metric Mapping is a particularly interesting family of physically meaningful diffeomorphic registration methods. PDE-constrained LDDMM methods are formulated as constrained variational problems, wher…
Compression of Neural Networks (NN) has become a highly studied topic in recent years. The main reason for this is the demand for industrial scale usage of NNs such as deploying them on mobile devices, storing them efficiently, transmitting them via band-limited channels and most importantly doing inference at scale. I…
This paper presents a bias-variance tradeoff of graph Laplacian regularizer, which is widely used in graph signal processing and semi-supervised learning tasks. The scaling law of the optimal regularization parameter is specified in terms of the spectral graph properties and a novel signal-to-noise ratio parameter, whi…
Deep convolutional neural networks (CNNs) used in practice employ potentially hundreds of layers and ,s of nodes. Such network sizes entail significant computational complexity due to the large number of convolutions that need to be carried out; in addition, a large number of parameters needs to be learned and…
Study proper sampling for X-ray transforms on simple surfaces.
New method constrains CNN filter frequencies to improve robustness.
In this paper, we show synchronization for a group of output passive agents that communicate with each other according to an underlying communication graph to achieve a common goal. We propose a distributed event-triggered control framework that will guarantee synchronization and considerably decrease the required comm…
Paper presents a unique method to recover signals from their bispectrum.
Developed a diffusion model on spherical data, addressing geometric and stochastic challenges.
In data science, it is often required to estimate dependencies between different data sources. These dependencies are typically calculated using Pearson's correlation, distance correlation, and/or mutual information. However, none of these measures satisfy all the Granger's axioms for an "ideal measure". One such ideal…
GNNs outperform NNs in interpolating bandlimited functions on Euclidean cubes.
Deep convolutional neural networks have led to breakthrough results in numerous practical machine learning tasks such as classification of images in the ImageNet data set, control-policy-learning to play Atari games or the board game Go, and image captioning. Many of these applications first perform feature extraction …
New method uses CNN for seismic inversion uncertainty quantification.
Paper advances sparse regularisation theory for measures with new kernel insights.
AaSP improves audio self-supervised learning by addressing aliasing issues.
Dual-space sampling tackles ill-conditioned inverse problems with Bayesian methods.
We develop methods to learn dictionaries invariant under group symmetries, useful in cryo-EM and tracking.