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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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148295443590 · Jun 202019922001200920172026
48 results for Representation Invariant Metrics

Using the thermodynamics formalism, we introduce a notion of intersection for projective Anosov representations, show analyticity results for the intersection and the entropy, and rigidity results for the intersection. We use the renormalized intersection to produce a Out(Γ)Out(Γ)-invariant Riemannian metric on the smooth …

2013-01-30abs ↗pdf ↗

Geodesic orbit metrics on real flag manifolds identified.

problem Classifying real flag manifolds with geodesic orbit metrics.
method Investigated invariant metrics on real flag manifolds, focusing on those where geodesics are orbits of one-parameter subgroups.
result Non-trivial geodesic orbit metrics exist on real flag manifolds, unlike in the complex case.

Method learns representations invariant to task-irrelevant details in reinforcement learning tasks.

problem Learning representations that are invariant to task-irrelevant details in reinforcement learning.
method Uses bisimulation metrics to learn robust latent representations that encode only task-relevant information.
result Demonstrates SOTA performance in modified visual MuJoCo tasks and a first-person driving task.

It is understood now that all projective (and conformal) invariants of Riemannian metrics can be found by a transparent construction based on representation theory. So this article with a partial and quite cumbersome construction of projective invariants become obsolete.

2003-05-30abs ↗pdf ↗

Unified framework for scale-invariant representation learning using MAPCA.

problem Learning invariant representations in data.
method Metric-Aware Principal Component Analysis (MAPCA) based on generalized eigenproblem.
result MAPCA provides a unified geometric language for various self-supervised learning objectives.

Deconfounds neural network representation similarity metrics to improve consistency and accuracy.

problem Confounding by population structure in similarity metrics like RSA and CKA.
method Covariate adjustment regression to adjust for confounders.
result Improves detection of semantically similar neural networks and consistency in transfer learning.

PeL separates sensory interface optimization from decision learning.

problem Optimizing sensory interfaces without task-specific information.
method Formal separation of perception and decision learning, using metrics for stability, informativeness, and geometry.
result Updates preserving invariants are orthogonal to decision gradients.

For generic torus-invariant metrics, eigenspaces are 2D and nodal sets are connected hypersurfaces.

problem Understanding spectral multiplicity and nodal sets for generic torus-invariant metrics.
method Analyzing real ΔgΔ_g-eigenspaces and nodal sets for generic TT-invariant metrics.
result For generic TT-invariant metrics, real ΔgΔ_g-eigenspaces are irreducible and have dimension at most 2, and nodal sets are connected hypersurfaces with specific properties.

We consider invariant Einstein metrics on the Stiefel manifold $V_q\bb{R} ^n$ of all orthonormal qq-frames in $\bb{R}^n$. This manifold is diffeomorphic to the homogeneous space $\SO(n)/\SO(n-q)$ and its isotropy representation contains equivalent summands. %This causes difficulty in the description of all $\SO(n)$-in…

2013-11-07abs ↗pdf ↗

In this article, we classify the set of asymptotic mass-like invariants for asymptotically hyperbolic metrics. It turns out that the standard mass is just one example (but probably the most important one) among the two families of invariants we find. These invariants are attached to finite-dimensional representations o…

2016-03-25abs ↗pdf ↗

Researchers classify invariant Hermitian structures on flag manifolds with parallel Bismut torsion.

problem Classifying invariant Hermitian structures with specific torsion properties on flag manifolds.
method Detailed analysis of invariant Hermitian structures on flag manifolds, proving conditions for parallel Bismut torsion.
result Conditions for the existence of parallel Bismut torsion on most flag manifolds.

We study invariant Einstein metrics on the Stiefel manifold VkRnSO(n)/SO(nk)V_k\mathbb{R}^n\cong \mathrm{SO}(n)/\mathrm{SO}(n-k) of all orthonormal kk-frames in Rn\mathbb{R}^n. The isotropy representation of this homogeneous space contains equivalent summands, so a complete description of GG-invariant metrics is not easy. In this …

2018-10-01abs ↗pdf ↗

We present a classification of the complete, simply connected, contact metric (κ,μ)(κ,μ)-spaces as homogeneous contact metric manifolds, by studying the base space of their canonical fibration. According to the value of the Boeckx invariant, it turns out that the base is a complexification or a para-complexification of a …

2017-04-05abs ↗pdf ↗

In this paper we give a spinorial representation of submanifolds of any dimension and codimension into Lie groups equipped with left invariant metrics. As applications, we get a spinorial proof of the Fundamental Theorem for submanifolds into Lie groups, we recover previously known representations of submanifolds in $\…

2016-09-20abs ↗pdf ↗

Given an exceptional compact simple Lie group GG we describe new left-invariant Einstein metrics which are not naturally reductive. In particular, we consider fibrations of GG over flag manifolds with a certain kind of isotropy representation and we construct the Einstein equation with respect to the induced left-inv…

2015-11-12abs ↗pdf ↗

Proposes Infomax and Domain-Independent Representations for robust causal inference.

problem Handling treatment selection bias and domain imbalance in causal inference with real-world data.
method Utilizes mutual information to learn domain-invariant representations that maximize predictive common information.
result Achieves state-of-the-art performance on causal effect inference across various data distributions.

In this paper, we classify compact simply connected cohomogeneity one manifolds up to equivariant diffeomorphism whose isotropy representation by the connected component of the principal isotropy subgroup has three or less irreducible summands. The manifold is either a bundle over a homogeneous space or an irreducible …

2010-06-02abs ↗pdf ↗

New measures quantify diversity of latent representations using metric space magnitude.

problem Evaluating the diversity of latent representations in machine learning models.
method Developed magnitude-based measures for latent representations, stable under data perturbations.
result Demonstrated superior performance across various domains and tasks.

Study compares metrics from negative curvature and quasi-Fuchsian representations.

problem Comparing metrics on surface groups from negative curvature and quasi-Fuchsian representations.
method Examines Teichmüller space as the intersection of two metric families.
result Teichmüller space is the only common part of the two metric families.

We consider the Ricci flow equation for invariant metrics on compact and connected homogeneous spaces whose isotropy representation decomposes into two irreducible inequivalent summands. By studying the corresponding dynamical system, we completely describe the behaviour of the homogeneous Ricci flow on this kind of sp…

2012-09-13abs ↗pdf ↗

New metric captures individual neuron tuning across neural networks.

problem Need a metric that respects individual neuron tuning across different neural networks.
method Derived a 'soft' permutation-based metric using optimal transport theory.
result Metric avoids counter-intuitive outcomes and captures geometric insights.

This article is a follow up of the previous article of the authors on the analytic surgery of eta- and rho-invariants. We investigate in detail the (Atiyah-Patodi-Singer)-rho-invariant for manifolds with boundary. First we generalize the cut-and-paste formula to arbitrary boundary conditions. A priori the rho-invariant…

2002-03-11abs ↗pdf ↗

We classify the holonomy algebras of manifolds admitting an indecomposable torsion free G2G_2^*-structure, i.e. for which the holonomy representation does not leave invariant any proper non-degenerate subspace. We realize some of these Lie algebras as holonomy algebras of left-invariant metrics on Lie groups.

2016-04-02abs ↗pdf ↗

Via a non degenerate symmetric bilinear form we identify the coadjoint representation with a new representation and so we induce on the orbits a simplectic form. By considering Hamiltonian systems on the orbits we study some features of them and finally find commuting functions under the corresponding Lie-Poisson brack…

2003-01-28abs ↗pdf ↗

Let G/HG/H be a compact homogeneous space, and let g^0\hat{g}_0 and g^1\hat{g}_1 be GG-invariant Riemannian metrics on G/HG/H. We consider the problem of finding a GG-invariant Einstein metric gg on the manifold G/H×[0,1]G/H\times [0,1] subject to the constraint that gg restricted to G/H×{0}G/H\times \{0\} and G/H×{1}G/H\times \{1\} co…

2017-10-05abs ↗pdf ↗

In this paper, new representations of a Bertrand curve pair in three dimensional Lie groups with bi-invariant metric are given. Besides, the spherical indicatrices of a Bertrand curve pair are obtain and the relations between the spherical indicatrices and new representations of Bertrand curve pair are shown.

2018-01-10abs ↗pdf ↗

This paper focuses on the study of open curves in a manifold M, and proposes a reparameterization invariant metric on the space of such paths. We use the square root velocity function (SRVF) introduced by Srivastava et al. in [11] to define a reparameterization invariant metric on the space of immersions M' = Imm([0,1]…

2015-07-23abs ↗pdf ↗

In this work we study the existence of homogeneous Einstein metrics on the total space of homogeneous fibrations such that the fibers are totally geodesic manifolds. We obtain the Ricci curvature of an invariant metric with totally geodesic fibers and some necessary conditions for such a metric to be Einstein in terms …

2009-05-19abs ↗pdf ↗

A new geometric metric identifies true data changes from parametrization artifacts in high-dimensional representations.

problem Quantifying representation drift in high-dimensional data using Euclidean or cosine distances can misattribute changes due to arbitrary parametrizations.
method Introducing the Fubini Study metric to identify representations that differ only by gauge transformations.
result The Fubini Study metric isolates intrinsic evolution by remaining invariant under gauge-induced fluctuations, providing a diagnostic for meaningful structural changes.

A family of naturally reductive pseudo-Riemannian spaces is constructed out of the representations of Lie algebras with ad-invariant metrics. We exhibit peculiar examples, study their geometry and characterize the corresponding naturally reductive homogeneous structure.

2010-07-27abs ↗pdf ↗

For a compact homogeneous space G/KG/K, we study the problem of existence of GG-invariant Riemannian metrics such that each eigenspace of the Laplacian is a real irreducible representation of GG. We prove that the normal metric of a compact irreducible symmetric space has this property only in rank one. Furthermore, w…

2017-07-05abs ↗pdf ↗

This work characterizes how data augmentation shapes neural representations.

problem Understanding the impact of data augmentation on neural network representations.
method Embedding neural network hidden representations into a metric space invariant to transformations, analyzing shape-space trajectories.
result Increasing data augmentation strength leads to well-behaved trajectories in the embedded space, and different augmentation types steer representations in distinct directions.

The classification of homogeneous compact Einstein manifolds in dimension six is an open problem. We consider the remaining open case, namely left-invariant Einstein metrics gg on G=SU(2)×SU(2)=S3×S3G = \mathrm{SU}(2) \times \mathrm{SU}(2) = S^3 \times S^3. Einstein metrics are critical points of the total scalar curvature functional …

2017-03-30abs ↗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.