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arXiv research

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.

168,695 papers · 148 categories

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122243365486 · Jun 202019922001200920172026
48 results for operator representations

The paper classifies and proves properties of symmetry breaking operators for specific groups.

problem Classifying and understanding symmetry breaking operators for de Sitter and Lorentz groups.
method Constructing and classifying differential symmetry breaking operators, proving localness, and showing sporadic nature.
result All symmetry breaking operators are differential and sporadic, not obtainable by residue formulas.

Develops a smooth operator framework for analyzing neural network representations.

problem Analyzing the geometry of feedforward neural network representations.
method Introduces a smooth operator-theoretic approach based on diffusion Markov operators derived from feature clouds.
result Establishes a stable operator-geometric framework for tracking training, width, and perturbation stability.

We give a complete classification of intertwining operators (symmetry breaking operators) between spherical principal series representations of G=O(n+1,1) and G'=O(n,1). We construct three meromorphic families of the symmetry breaking operators, and find their distribution kernels and their residues at all poles explic…

2013-10-11abs ↗pdf ↗

User response prediction makes a crucial contribution to the rapid development of online advertising system and recommendation system. The importance of learning feature interactions has been emphasized by many works. Many deep models are proposed to automatically learn high-order feature interactions. Since most featu…

2019-04-02abs ↗pdf ↗

Manifold learning seeks a low dimensional representation that faithfully captures the essence of data. Current methods can successfully learn such representations, but do not provide a meaningful set of operations that are associated with the representation. Working towards operational representation learning, we endow…

2019-08-20abs ↗pdf ↗

In this paper we study unitary braid group representations associated with Majorana Fermions. Majorana Fermions are represented by Majorana operators, elements of a Clifford algebra. The paper recalls and proves a general result about braid group representations associated with Clifford algebras, and compares this resu…

2017-10-12abs ↗pdf ↗

Infinitesimal holomorphic realizations for the Schrödinger-Weil representation and the discrete series representations of the Jacobi group are constructed. Explicit expressions of the basic differential operators are obtained. The squeezed states for the unitary irreducible representation of the Jacobi group are introd…

2008-12-02abs ↗pdf ↗

Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear operations. However, these graph-dependent linear operations are d…

2017-11-03abs ↗pdf ↗

Study shows how information loss and operation loss are related in feature representations.

problem Understanding the relationship between information loss and operation loss in feature representations.
method Analyzes the interplay between weak information loss and operation loss in continuous representations.
result Specific forms of vanishing information loss imply vanishing MPE loss in classification.

This work proposes a method to learn sparse representations that are more efficient for large-scale data retrieval.

problem Efficient retrieval of high-dimensional representations from large databases is computationally challenging.
method The approach minimizes the number of floating-point operations (FLOPs) by learning sparse embeddings with uniform non-zero entries.
result The proposed method achieves a similar or better speed-vs-accuracy tradeoff compared to existing baselines.

The Lagrangian representation of multi-Hamiltonian PDEs has been introduced by Y. Nutku and one of us (MVP). In this paper we focus on systems which are (at least) bi-Hamiltonian by a pair A1A_1, A2A_2, where A1A_1 is a hydrodynamic-type Hamiltonian operator. We prove that finding the Lagrangian representation is equiv…

2016-10-06abs ↗pdf ↗

In this largely expository paper we give a self-contained treatment of the Dirac operator. Emphasizing the algebraic point of view we first sketch the necessary prerequisites from Clifford algebras and their representations and then define (and characterize) spin structures and the corresponding Spin-Dirac operator pur…

2000-05-24abs ↗pdf ↗

A new metric compares dynamical systems using operator eigenvalues.

problem Comparing and interpolating nonlinear dynamical systems from trajectory data.
method Representing systems as distributions of operator eigenvalues and projectors, defining a spectral-Grassmann Wasserstein metric.
result The proposed metric outperforms standard operator-based distances in machine learning applications.

We prove that the Casimir operator acting on sections of a homogeneous vector bundle over a generalized flag manifold naturally extends to an invariant differential operator on arbitrary parabolic geometries. We study some properties of the resulting invariant operators and compute their action on various special types…

2007-08-23abs ↗pdf ↗

We present a holomorphic representation of the Jacobi algebra hnsp(n,R)\mathfrak{h}_n\rtimes \mathfrak{sp}(n,\R) by first order differential operators with polynomial coefficients on the manifold Cn×Dn\mathbb{C}^n\times \mathcal{D}_n. We construct the Hilbert space of holomorphic functions on which these differential operators a…

2006-04-18abs ↗pdf ↗

Classifies and constructs intertwining differential operators between vector bundles over real projective space.

problem Classifying and constructing intertwining differential operators between vector bundles over RP2\mathbb{RP}^2.
method Utilizes SL(3,R)SL(3,\mathbb{R})-intertwining differential operators, BGG resolution, and representation theory.
result Irreducible unitary highest weight modules of SU(1,2)SU(1,2) at reduction points classified by Cartan and PRV operators.

New neural operators learn structured patterns efficiently.

problem Learning and representing complex, structured patterns in data.
method Sparse autoencoder neural operators (SAE-NOs) parameterize concepts as functions, enabling efficient and structured representation.
result SAE-FNOs learn localized patterns and generalize across different scales and discretizations.

We study the geometry and partial differential equations arising from the consideration of group-determinants, and representation theory. The simplest and most striking such example is undoubtedly that of the Humbert operator, associated with the cyclic group Z/3Z. This operator appears as a natural extension of the La…

2019-10-28abs ↗pdf ↗

A book representation of a graph is a particular way of embedding a graph in three dimensional space so that the vertices lie on a circle and the edges are chords on disjoint topological disks. We describe a set of operations on book representations that preserves ambient isotopy, and apply these operations to K6K_6, t…

2017-03-24abs ↗pdf ↗

A representation of the Jacobi algebra h1su(1,1)\mathfrak{h}_1\rtimes \mathfrak{su}(1,1) by first order differential operators with polynomial coefficients on the manifold C×D1\mathbb{C}\times \mathcal{D}_1 is presented. The Hilbert space of holomorphic functions on which the holomorphic first order differential operators with …

2004-08-17abs ↗pdf ↗

Let G be a compact connected semisimple Lie group and let H\subset G be a closed connected subgroup such that rank(G)=rank(H) and G/H is a symmetric space. Given an irreducible representation of H, we define a Dirac operator D and determine the representations of G in the kernel of D. Moreover, we show that any irreduc…

2010-08-25abs ↗pdf ↗

We study the character of the infinite wedge projective representation of the algebra of differential operators on the circle. We prove quasi-modularity of this character and also compute certain generating functions for traces of differential operators which we call correlation functions. These correlation functions a…

1997-12-09abs ↗pdf ↗

Paper learns Koopman operator from sparse data, escaping function space constraints.

problem Learning Koopman operator from non-closed function spaces.
method Operator stochastic approximation algorithm using conditional mean embeddings (CME).
result Online sparse learning algorithm with trajectory-based sampling guarantees.

Near-optimal rates for multi-task learning with shared representations.

problem Approximation and statistical complexity of learning multiple operators.
method Multiple Neural Operators (MNO) architecture and comparison with DeepONet.
result Near-optimal upper and lower bounds for approximation and generalization.

The concept of F-algebra and its representation can be extended to an arbitrary bundle. We define operations of fibered F-algebra in fiber. The paper presents the representation theory of of fibered F-algebra as well as a comparison of representation of F-algebra and of representation of fibered F-algebra.

2007-02-19abs ↗pdf ↗

Study on eigenvalues of Laplace operator on 1-forms for symmetric spaces.

problem Investigating the first eigenvalue of the Laplace operator on 1-forms in compact inner symmetric spaces.
method Analyzing the Casimir eigenvalue of the highest root for the isotropy representation.
result The first eigenvalue of the Laplace operator on 1-forms is the Casimir eigenvalue of the highest root.

In this paper, we describe the group SpinT (n) and give some properties of this group. We construct SpinT spinor bundle S by means of the spinor representation of the group SpinT (n) and define covariant derivative operator and Dirac operator on S. Finally, Schrodinger-Lichnerowicz-type formula is derived by using thes…

2015-08-19abs ↗pdf ↗

The study quantifies how many objects can be linearly classified under all views.

problem Understanding the expressivity of group-equivariant representations.
method Generalization of Cover's Function Counting Theorem to quantify separable dichotomies.
result The fraction of separable dichotomies is determined by the fixed space dimension of the group action.

In this paper we provide an integral representation of the fractional Laplace-Beltrami operator for general riemannian manifolds which has several interesting applications. We give two different proofs, in two different scenarios, of essentially the same result. One of them deals with compact manifolds with or without …

2017-04-20abs ↗pdf ↗

New method tests conditional independence using spectral representations.

problem Untestable conditional independence in many settings.
method Spectral representations of partial covariance operators, bi-level contrastive learning.
result Asymptotic validity and power guarantees for CI testing.

We introduce a new class of natural, explicitly defined, transversally elliptic differential operators over manifolds with compact group actions. Under certain assumptions, the symbols of these operators generate all the possible values of the equivariant index. We also show that the components of the representation-va…

2008-05-21abs ↗pdf ↗

Let $\X\simeq G/K$ be a Riemannian symmetric space of non-compact type, $\widetilde \X$ its Oshima compactification, and $(π,\mathrm{C}(\widetilde \X))$ the regular representation of GG on $\widetilde \X$. We study integral operators on $\widetilde \X$ of the form π(f)π(f), where ff is a rapidly falling function on GG

2011-02-24abs ↗pdf ↗

We discuss a peculiar interplay between the representation theory of the holonomy group of a Riemannian manifold, the Weitzenboeck formula for the Hodge-Laplace operator on forms and the Lichnerowicz formula for twisted Dirac operators. For quaternionic Kaehler manifolds this leads to simple proofs of eigenvalue estima…

2000-01-11abs ↗pdf ↗

Graph Neural Networks (GNNs), which generalize deep neural networks to graph-structured data, have drawn considerable attention and achieved state-of-the-art performance in numerous graph related tasks. However, existing GNN models mainly focus on designing graph convolution operations. The graph pooling (or downsampli…

2019-11-14abs ↗pdf ↗

Study shows neural operators can efficiently solve complex reaction-diffusion systems.

problem Efficiently solving nonlinear reaction-diffusion systems using neural operators.
method Laplacian-based neural operators applied to a generalized Gierer-Meinhardt system.
result Explicit approximation error bounds established for neural operators in terms of network parameters.