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.
Data structure affects deep learning performance, study finds.
Improved nuclear cross section fitting with weighted Levenberg-Marquardt method.
We show that log-periodic power-law (LPPL) functions are intrinsically very hard to fit to time series. This comes from their sloppiness, the squared residuals depending very much on some combinations of parameters and very little on other ones. The time of singularity that is supposed to give an estimate of the day of…
Improved financial market calibration reveals large excess volatility.
We address the problem of parameter estimation in models of systems biology from noisy observations. The models we consider are characterized by simultaneous deterministic nonlinear differential equations whose parameters are either taken from in vitro experiments, or are hand-tuned during the model development process…
The goal of this document is to provide a pedagogical introduction to the main concepts underpinning the training of deep neural networks using gradient descent; a process known as backpropagation. Although we focus on a very influential class of architectures called "convolutional neural networks" (CNNs) the approach …
Develops neural network for directed hypergraphs for node classification.
LayerNorm transformers have dead directions that can be read from their parameters alone.
We introduce the notion of directed diagrammatic reducibility which is a relative version of diagrammatic reducibility. Directed diagrammatic reducibility has strong group theoretic and topological consequences. A multi-relator version of the Freiheitssatz in the presence of directed diagrammatic reducibility is given.…
Unsupervised method discovers interpretable directions in GAN latent space.
The paper studies kernel smoothing and mean shift for directional data, deriving convergence rates and mode estimation.
During the last two decades, we easilly see that the World Wide Web's link structure is modeled as the directed graph. In this paper, we will model the World Wide Web's link structure as the directed hypergraph. Moreover, we will develop the PageRank algorithm for this directed hypergraph. Due to the lack of the World …
DimeNet uses directional message passing to improve molecular predictions.
DiMMSB models directed mixed membership networks, identifying distinct community structures.
PyTorch Geometric Signed Directed fills the gap for GNNs on signed and directed graphs.
In the bordered Floer theory, gluing thickened torus of positive meridional Dehn twist to the boundary of a knot complement result in the knot complement of increased framing. For a fixed knot K, we construct a direct system of positively framed knot complements and study the direct limit. We also study the morphism sp…
In this study, we define a new type of direction curves in the Euclidean 3-space such as osculating-direction curve. We give the characterizations for these curves. Moreover, we obtain the relationships between osculating direction curves and some special curves such as helix, slant helix or rectifying curves.
Directed graphs occur throughout statistical modeling of networks, and exchangeability is a natural assumption when the ordering of vertices does not matter. There is a deep structural theory for exchangeable undirected graphs, which extends to the directed case via measurable objects known as digraphons. Using digraph…
DEDACT breaks down feature importance into direct and associative components.
A goal in network science is the geometrical characterization of complex networks. In this direction, we have recently introduced Forman's discretization of Ricci curvature to the realm of undirected networks. Investigation of this edge-centric network measure, Forman-Ricci curvature, in diverse model and real-world un…
FastMap-D embeds directed graphs using potential fields.
New toolkit for directed distances improves flexibility of OT problems.
This paper is devoted to the framework of direct limit of anchored Banach bundles over a convenient manifold which is a direct limit of Banach manifold. In particular we give a criterion of integrability for distributions on such convenient manifolds which are locally direct limits of particular sequences of Banach anc…
Study of Betti numbers in prodsimplicial complexes for directed graphs, focusing on DNA recombination.
Study on rigidity of translating hypersurfaces not in graphical direction.
Spectral clustering for directed graphs using likelihood estimation.
Proposes a novel approach using vector cross product to preserve directional edges in directed graphs.
Novel GNN for signed and directed networks using magnetic signed Laplacian.
Local causal structure learning aims to discover and distinguish direct causes (parents) and direct effects (children) of a variable of interest from data. While emerging successes have been made, existing methods need to search a large space to distinguish direct causes from direct effects of a target variable \emph{T…
New method identifies valid IVs for bi-directional MR with invalid instruments.
This paper considers the problem of embedding directed graphs in Euclidean space while retaining directional information. We model a directed graph as a finite set of observations from a diffusion on a manifold endowed with a vector field. This is the first generative model of its kind for directed graphs. We introduce…
Proposes a copula-based model for multi-view clustering with directional dependency.
In Carnot groups, directional pliability allows curve extensions and approximations.
The study examines principal directions and curvatures of Lagrangian submanifolds.
In this note we give a construction of a smooth Riemannian metric on R^n which is standard Euclidean outside a compact set K and such that it has N = n(n + 1)=2 invisible directions, meaning that all geodesics lines passing through the set K in these directions remain the same straight lines on exit. For example in the…
Study geodesic trees and exceptional directions in FPP on hyperbolic groups.
In this paper, we define the curvature dimension inequalities CD(m, K) on finite directed graphs modifying the case of undirected graphs. As a main result, we evaluate m and K on finite directed graphs.
This letter presents a new spectral-clustering-based approach to the subspace clustering problem. Underpinning the proposed method is a convex program for optimal direction search, which for each data point d finds an optimal direction in the span of the data that has minimum projection on the other data points and non…
GANs excel at learning high dimensional distributions, but they can update generator parameters in directions that do not correspond to the steepest descent direction of the objective. Prominent examples of problematic update directions include those used in both Goodfellow's original GAN and the WGAN-GP. To formally d…
In this paper, we characterize and classify all surfaces endowed with canonical principal direction relative to a space-like and light-like, constant direction in Minkowski 3-spaces.
We introduce a novel harmonic analysis for functions defined on the vertices of a strongly connected directed graph of which the random walk operator is the cornerstone. As a first step, we consider the set of eigenvectors of the random walk operator as a non-orthogonal Fourier-type basis for functions over directed gr…
Improves community detection in directed networks with theoretical guarantees.
Study relaxes identification assumptions for natural direct effects in non-randomized settings.
Paper defines a new dimension to measure self-directed learning complexity.
New method for community detection in sparse directed SBMs with exact recovery guarantees.
Proposes a new model for traffic flow on directed graphs.
Paper proves linear convergence of SCMS algorithm for directional data.
New method clusters directed graphs using Koopman operators.