arXiv research

Proves h-principle for loose Legendrian embeddings in contact topology.

Embedding principle explains loss landscape of deep neural networks.

Study proves h-principles for curves in bracket-generating distributions.

Study proves finiteness for distance functions on curved surfaces with controlled curvature.

Local-to-global principle for Morse actions on symmetric spaces.

We embed KKT points in neural networks of different sizes.

We study weak solutions to degenerate quasilinear elliptic equations, involving first order terms, in unbounded tubular domains. In particular we show that, under suitable hypotheses, the weak comparison principle holds if the domain is narrow enough.

Network embedding has proved extremely useful in a variety of network analysis tasks such as node classification, link prediction, and network visualization. Almost all the existing network embedding methods learn to map the node IDs to their corresponding node embeddings. This design principle, however, hinders the ex…

Curvature measures uniquely determined by invariance under embeddings.

Paper uses advanced math to embed complex shapes smoothly.

Study curve flows with global forcing terms using a distance comparison principle.

Paper generalizes wrinkled embedding concept to jet spaces.

The Nash-Kuiper Theorem states that the collection of $C^1$-isometric embeddings from a Riemannian manifold $M^n$ into $\mathbb{E}^N$ is $C^0$-dense within the collection of all smooth 1-Lipschitz embeddings provided that $n < N$. This result is now known to be a consequence of Gromov's more general $h$-principle. Ther…

We propose a principle for exploring context in machine learning models. Starting with a simple assumption that each observation may or may not depend on its context, a conditional probability distribution is decomposed into two parts: context-free and context-sensitive. Then by employing the log-linear word production…

A framework for network embedding using VDS principles.

Curve shortening flow shrinks curves to points.

We establish an $h$-principle for exact Lagrangian embeddings with concave Legendrian boundary. We prove, in particular, that in the complement of the unit ball $B$ in the standard symplectic $\R^{2n}, 2n\geq 6$, there exists an embedded Lagrangian $n$-disc transversely attached to $B$ along its Legendrian boundary.

New self-shrinkers found in higher dimensions.

On a Weinstein manifold, we define a constructible co/sheaf of categories on the skeleton. The construction works with arbitrary coefficients, and depends only on the homotopy class of a section of the Lagrangian Grassmannian of the stable symplectic normal bundle. The definition is as follows. Take any, possibly high …

We prove prove a bridge principle at infinity for area-minimizing surfaces in the hyperbolic space $\mathbb{H}^3$, and we use it to prove that any open, connected, orientable surface can be properly embedded in $\mathbb{H}^3$ as an area-minimizing surface. Moreover, the embedding can be constructed in such a way that t…

A new method to break down insurance costs into risk and uncertainty.

Sequential and temporal data arise in many fields of research, such as quantitative finance, medicine, or computer vision. A novel approach for sequential learning, called the signature method and rooted in rough path theory, is considered. Its basic principle is to represent multidimensional paths by a graded feature …

Minimal surfaces reflect across spheres, proving annulus uniqueness.

The paper proves isometric embeddings for smooth manifolds.

Paper confirms Yau's conjecture about sphere eigenvalues.

This paper provides a geometrical derivation of the Hybrid Minimum Principle (HMP) for autonomous hybrid systems whose state manifolds constitute Lie groups $(G,\star)$ which are left invariant under the controlled dynamics of the system, and whose switching manifolds are defined as smooth embedded time invariant subma…

Normal forms and isotropic embeddings via Euler-like vector fields.

Proves existence of strongly overtwisted contact structures on 3-manifolds.

Study asymptotic behavior of Weingarten surfaces at infinity.

Network embedding aims to learn the low-dimensional representations of vertexes in a network, while structure and inherent properties of the network is preserved. Existing network embedding works primarily focus on preserving the microscopic structure, such as the first- and second-order proximity of vertexes, while th…

The paper extends results on minimal hypersurfaces in Riemannian manifolds to higher dimensions.

The paper solves symplectic embedding problems in higher dimensions, proving new embedding conditions.

Smooth curves with specific curvature can be closely approximated.

In this paper, we show that any open orientable surface S can be properly embedded in H^3 as a minimizing H-surface for any 0<=H<1. We obtained this result by proving a version of the bridge principle at infinity for H-surfaces. We also show that any open orientable surface S can be nonproperly embedded in H^3 as a min…

The paper calculates prices for multi-step barrier options under the Black-Scholes model.

In this paper we study the embedding of Riemannian manifolds in low codimension. The well-known result of Nash and Kuiper says that any short embedding in codimension one can be uniformly approximated by $C^1$ isometric embeddings. This statement clearly cannot be true for $C^2$ embeddings in general, due to the classi…

Most existing word embedding approaches do not distinguish the same words in different contexts, therefore ignoring their contextual meanings. As a result, the learned embeddings of these words are usually a mixture of multiple meanings. In this paper, we acknowledge multiple identities of the same word in different co…

LASE learns graph embeddings by unrolling GD iterations into a neural network.

Two methods factor out prior knowledge from low-dimensional embeddings.

Instance embeddings are an efficient and versatile image representation that facilitates applications like recognition, verification, retrieval, and clustering. Many metric learning methods represent the input as a single point in the embedding space. Often the distance between points is used as a proxy for match confi…

ELM improves neural model embeddings for long-tail learning.

We introduce a nonlinear method for directly embedding large, sparse, stochastic graphs into low-dimensional spaces, without requiring vertex features to reside in, or be transformed into, a metric space. Graph data and models are prevalent in real-world applications. Direct graph embedding is fundamental to many graph…

In the mid-1980's, M. Gromov used his machinery of the $h$-principle to prove that there exists totally real embeddings of $S^3$ into $\mathbb{C}^3$. Subsequently, Patrick Ahern and Walter Rudin explicitly demonstrated such a totally real embedding. In this paper, we consider the generic situation for such embeddings, …

ULES embeds dynamic networks with stability guarantees.

Combines OT and PCA for DR, preserving clusters.

This paper proposes a novel architecture, termed multiscale principle of relevant information (MPRI), to learn discriminative spectral-spatial features for hyperspectral image (HSI) classification. MPRI inherits the merits of the principle of relevant information (PRI) to effectively extract multiscale information embe…

Proposes Gromov-Wasserstein methods for multi-view embedding.

Improves visualization of high-dimensional data by correcting misleading artifacts in neighbor embedding methods.