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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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48 results for hyperbolic manifold learning

The hyperbolic manifold is a smooth manifold of negative constant curvature. While the hyperbolic manifold is well-studied in the literature, it has gained interest in the machine learning and natural language processing communities lately due to its usefulness in modeling continuous hierarchies. Tasks with hierarchica…

2019-03-18abs ↗pdf ↗

This work tackles manifold regression onto hyperbolic space for tree classification and taxonomy extension.

problem Performing manifold-valued regression onto an hyperbolic space for tree classification and taxonomy extension.
method Formulated as a manifold regression task in hyperbolic space, proposed a parametric deep learning model and a non-parametric kernel method.
result Hyperbolic-based estimators significantly outperform Euclidean space methods in taxonomy expansion.

A new method for precise user targeting in advertising using hyperbolic manifold learning.

problem Improving user satisfaction in targeted advertising by overcoming skepticism and spam perception.
method Proposes a Multi-Manifold Learning framework to learn hierarchical user and ad representations in the hyperbolic space.
result Demonstrates improved performance in user targeting and prediction accuracy on both public datasets and a large-scale commercial system.

The paper defines two types of hyperbolicity for complex manifolds and proves related results.

problem Defining and studying hyperbolicity for a broader class of complex manifolds.
method Introducing SKT hyperbolicity and Gauduchon hyperbolicity, proving results using SKT and Gauduchon metrics.
result Every SKT hyperbolic manifold is also Kobayashi/Brody hyperbolic and every Gauduchon hyperbolic manifold is divisorially hyperbolic.

Unified framework for Riemannian deep learning across manifold-valued representations.

problem Deep learning on manifold-valued representations often relies on Euclidean approximations or costly geometric operations.
method Develops reusable neural modules, manifold-specific network architectures, and geometric designs.
result Generalizes batch normalization and multinomial logistic regression to broader classes of manifolds.

Study shows partial hyperbolicity leads to Anosov dynamics in 3-manifolds.

problem Understanding dynamics in hyperbolic 3-manifolds and Seifert manifolds.
method Classification of partially hyperbolic diffeomorphisms and pseudo-Anosov dynamics.
result Complete classification of partially hyperbolic diffeomorphisms in hyperbolic 3-manifolds and Seifert manifolds.

Study shows certain 4D hyperbolic links don't contain geodesic 3-manifolds.

problem Proving certain hyperbolic link complements don't contain geodesic 3-manifolds.
method Analyzing hyperbolic link complements of 2-tori in S^4.
result Proves certain hyperbolic link complements do not contain closed embedded totally geodesic hyperbolic 3-manifolds.

This paper is the second in a series whose goal is to understand the structure of low-volume complete orientable hyperbolic 3-manifolds. Using Mom technology, we prove that any one-cusped hyperbolic 3-manifold with volume <= 2.848 can be obtained by a Dehn filling on one of 21 cusped hyperbolic 3-manifolds. We also sho…

2007-05-30abs ↗pdf ↗

Complex hyperbolic manifolds with many totally geodesic submanifolds are arithmetic.

problem Characterizing arithmeticity of complex hyperbolic manifolds with certain submanifolds.
method Developing superrigidity theorems for complex hyperbolic lattices and proving nonexistence of certain maps.
result Finite volume complex hyperbolic nn-manifolds containing infinitely many maximal totally geodesic submanifolds of dimension at least two are arithmetic.

Study on new hyperbolicity notions for non-Kähler manifolds and their deformations.

problem Analyzing new hyperbolicity notions for non-Kähler complex manifolds.
method Introducing and analyzing two new notions of hyperbolicity for compact complex non-Kähler manifolds, and studying their behavior under smooth modifications.
result Established openness results for pp-HS hyperbolicity and pp-Kähler hyperbolicity under holomorphic deformations.

The Euler number of special symplectic hyperbolic manifolds is positive.

problem Understanding the Euler number of symplectic hyperbolic manifolds.
method Study L2L^{2}-harmonic forms on the universal covering space and prove the Singer conjecture.
result The Euler number of a special symplectic manifold satisfies (1)nχ(X)>0(-1)^{n}χ(X)>0.

The study explores geometric properties of hyperbolic cohomology classes on Kähler manifolds.

problem Understanding the geometric effects of hyperbolic cohomology classes on Kähler manifolds.
method Introducing Kähler topologically hyperbolic manifolds and proving spectral gap theorems for positive holomorphic Hermitian vector bundles.
result Kähler topologically hyperbolic manifolds are not uniruled nor bimeromorphic to compact Kähler manifolds with trivial first real Chern class.

The study finds a bound on the shortest geodesics in hyperbolic 3-manifolds.

problem Finding bounds on the shortest geodesics in hyperbolic 3-manifolds.
method Establishing an upper bound for the length of the nthn^{th} shortest closed geodesic in terms of the volume of the manifold.
result An upper bound for the length of the nthn^{th} shortest closed geodesic in terms of the volume of the manifold.

Stability of positive mass theorem for hyperbolic manifolds studied.

problem Stability of the positive mass theorem for asymptotically hyperbolic manifolds.
method Adapted intrinsic flat distance approach to show stability for a class of manifolds.
result Stability of the positive mass theorem for a class of asymptotically hyperbolic graphical manifolds.

Learning from graph-structured data is an important task in machine learning and artificial intelligence, for which Graph Neural Networks (GNNs) have shown great promise. Motivated by recent advances in geometric representation learning, we propose a novel GNN architecture for learning representations on Riemannian man…

2019-10-28abs ↗pdf ↗