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.
A low-rank tensor model simplifies multi-dimensional Markov chains.
In this paper, we analyzed the physical meaning of scalar curvatures for a generalized Riemannian space. It is developed the Madsen's formulae for pressures and energy-densities with respect to the corresponding energy-momentum tensors. After that, the energy-momentum tensors, pressures, energy-densities and state-para…
We give new necessary and sufficient conditions on the Weyl tensor for generalized Robertson-Walker (GRW) space-times to be perfect-fluid space-times. For GRW space-times, we determine the form of the Ricci tensor in all the O(n)-invariant subspaces provided by Gray's decomposition of the gradient of the Ricci tensor. …
A perfect-fluid space-time of dimension n>3 with 1) irrotational velocity vector field, 2) null divergence of the Weyl tensor, is a generalised Robertson-Walker space-time with Einstein fiber. Condition 1) is verified whenever pressure and energy density are related by an equation of state. The contraction of the Weyl …
Classifies weakly Einstein hypersurfaces in spaces of constant curvature.
A new tensor network method for image classification reduces computation cost.
Paper introduces TSSDMN for modeling dynamic multilayer networks.
Tensor networks improve anomaly detection at LHC for new physics.
OpEvo automates tensor operator optimization for better efficiency.
Ansor generates high-performance tensor programs for deep learning.
Study finds all hyper-Kähler 4-manifolds with specific symmetries and structures.
Tensor decompositions are invaluable tools in analyzing multimodal datasets. In many real-world scenarios, such datasets are far from being static, to the contrary they tend to grow over time. For instance, in an online social network setting, as we observe new interactions over time, our dataset gets updated in its "t…
Study of hypersurfaces in curved spaces with specific curvature properties.
A toy model shows how locality can emerge in the universe's Hamiltonian and initial state.
Tensor networks improve medical image classification performance.
Paper learns meaningful state and action representations from MDP trajectories.
In tensor completion tasks, the traditional low-rank tensor decomposition models suffer from the laborious model selection problem due to their high model sensitivity. In particular, for tensor ring (TR) decomposition, the number of model possibilities grows exponentially with the tensor order, which makes it rather ch…
New method for inferring Markov chains from large state spaces, applied to epidemic models.
We propose a restricted class of tensor network state, built from number-state preserving tensors, for supervised learning tasks. This class of tensor network is argued to be a natural choice for classifiers as (i) they map classical data to classical data, and thus preserve the interpretability of data under tensor tr…
A formula for the Riemannian metric tensor of differentiable manifolds of linear dynamical systems of same McMillan degree is presented in terms of their transfer function matrices. The necessary calculations for its application to ARMA and state space overlapping parametrizations are drafted. The importance of this ap…
FunBaT extends Tucker decomposition to handle continuous-indexed tensor data.
Paper proposes a new method for density estimation using tree tensor-network states.
This work proposes a novel approach for multiple time series forecasting. At first, multi-way delay embedding transform (MDT) is employed to represent time series as low-rank block Hankel tensors (BHT). Then, the higher-order tensors are projected to compressed core tensors by applying Tucker decomposition. At the same…
No free lunch theorem formalized for tensor network models.
The difference tensor C.R - R.C of Einstein manifolds, some quasi-Einstein manifolds and Roter type manifolds, of dimension n > 3, satisfy the following curvature condition: (A) C.R - R.C = Q(S,C) - (k /(n-1)) Q(g,C). We investigate hypersurfaces M in space forms N satisfying (A). The main result states that if the ten…
New model uses PEPS for image classification, outperforming tree-like networks.
We review progress on the generalized Witten conjecture and some of its major ingredients. This conjecture states that certain intersection numbers on the moduli space of higher spin curves assemble into the logarithm of the tau function of a semiclassical limit of the r-th Gelfand-Dickey (or KdV_r) hierarchy. Addition…
This work introduces a tensor-based method to perform supervised classification on spatiotemporal data processed in an echo state network. Typically when performing supervised classification tasks on data processed in an echo state network, the entire collection of hidden layer node states from the training dataset is …
Efficiently factorize tensors in streaming data with coreset selection.
Tensor neural network improves human pose classification from 3D skeleton data.
Tensor networks improve image classification but require more expressive states.
Warped product manifolds with p-dimensional base, p=1,2, satisfy some curvature conditions of pseudosymmetry type. These conditions are formed from the metric tensor g, the Riemann-Christoffel curvature tensor R, the Ricci tensor S and the Weyl conformal curvature C of the considered manifolds. The main result of the p…
We introduce a new feature map for barcodes that arise in persistent homology computation. The main idea is to first realize each barcode as a path in a convenient vector space, and to then compute its path signature which takes values in the tensor algebra of that vector space. The composition of these two operations …
This paper aims to study the -curvature tensor on relativistic space-times. The energy-momentum tensor T of a space-time is semi-symmetric given that the -curvature tensor is semi-symmetric whereas energy-momentum tensor T of a space-time having a divergence free -curvature tensor is of Codazzi type. A space-t…
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
KReTTaH uses tensor trains and Hadamard overparameterization for fast, interpretable multi-way data imputation.
This paper introduces matrix product state (MPS) decomposition as a new and systematic method to compress multidimensional data represented by higher-order tensors. It solves two major bottlenecks in tensor compression: computation and compression quality. Regardless of tensor order, MPS compresses tensors to matrices …
Neural-Network Quantum States have been recently introduced as an Ansatz for describing the wave function of quantum many-body systems. We show that there are strong connections between Neural-Network Quantum States in the form of Restricted Boltzmann Machines and some classes of Tensor-Network states in arbitrary dime…
Defines a new tensor related to special geometric spaces.
Extends geometrical description of tensor manifolds in tree-based formats.
Tensor networks improve unsupervised learning performance.
Paper studies second order symmetric parallel tensors in generalized f.pk-space forms.
Researchers found indecomposable Killing tensor fields on quaternionic projective spaces.
DCTN uses tensor networks for image classification, achieving state-of-the-art results.
The paper classifies Killing tensor fields on Riemannian symmetric spaces.
A vector field on a Riemannian manifold is called conformal Killing if it generates one-parameter group of conformal transformations. The class of conformal Killing symmetric tensor fields of an arbitrary rank is a natural generalization of the class of conformal Killing vector fields, and appears in different geometri…
Quantum computers will work by evolving a high tensor power of a small (e.g. two) dimensional Hilbert space by local gates, which can be implemented by applying a local Hamiltonian H for a time t. In contrast to this quantum engineering, the most abstract reaches of theoretical physics has spawned topological models ha…
The Riemannian Bures metric on the space of (normalized) complex positive matrices is used for parameter estimation of mixed quantum states based on repeated measurements just as the Fisher information in classical statistics. It appears also in the concept of purifications of mixed states in quantum physics. Here we d…