In this paper, we study global existence and blow up properties to norm preserving non-local heat flows. We first study two kinds of norm preserving non-local flows and prove that these flows have the global solutions. Finally, we give a example to show that one kind of this heat flow may blow up in $L^{\in…
arXiv research
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Proposes a new regression method using -norms for non-Gaussian noise.
General lower bounds on neural network approximation in L^p norm.
In this paper we prove several results on the geometry of surfaces immersed in with small or bounded norm of . For instance, we prove that if the norm of and the norm of , , are sufficiently small, then such a surface is graphical away from its boundary. We also prove …
After appropriate normalizations an embedded disk whose second fundamental form has large norm contains a multi-valued graph, provided the L^P norm of the mean curvature is sufficiently small. This generalizes to non-minimal surfaces a well known result of Colding and Minicozzi.
Given a sequence of complete Riemannian manifolds of the same dimension, we construct a complete Riemannian manifold such that for all the -norm of the Riesz transform on dominates the -norm of the Riesz transform on for all . Thus we establish the following dichoto…
Infinite diameter proved for contractible loops space.
The paper extends inequalities for convex bodies to higher dimensions and various norms.
For a symplectic manifold let be the corresponding Poisson bracket. In this note we prove that the functional is lower-semicontinuous with respect to the -norm on when and , extending previous rigidity results for $…
We study stability and local minimizing properties of - norms of Riemannian curvature tensor denoted by by variational methods. We compute the Hessian of at compact rank 1 symmetric spaces and prove that they are stable for for certain values of p > 2. A similar resu…
The paper examines gradient and Riesz transform estimates under Ricci lower bounds.
Simple regional perturbations maintain model transferability while reducing adversarial example distortion.
In this paper we propose and investigate a novel nonlinear unit, called unit, for deep neural networks. The proposed unit receives signals from several projections of a subset of units in the layer below and computes a normalized norm. We notice two interesting interpretations of the unit. First…
Generalizes Sobolev IPM for graph-based measures using Orlicz geometric structure.
A holonomic space is a normed vector space, , a subgroup, , of and a group-norm, , with a convexity property. We prove that with the metric , is a metric space which is locally isometric to a Euclidean ball. Given a Sasaki-ty…
This paper analyzes shallow ReLU networks in L^p and Sobolev spaces, focusing on approximation and generalization.
New adversarial examples with structured distortion sets improve robustness and perceptibility.
We investigate stability and local minimizing properties of the Riemannian functional defined by the L^p norm of the curvature tensor on the space of Riemannian metrics on a closed manifold. Riemannian metrics with constant curvature and products of such metrics are critical points of this functional. We prove that the…
This paper presents a general framework for norm-based capacity control for weight normalized deep neural networks. We establish the upper bound on the Rademacher complexities of this family. With an normalization where , and , we discuss properties of a width-independent ca…
We extend a result of the second author \cite[Theorem 1.1]{soggekaknik} to dimensions which relates the size of -norms of eigenfunctions for to the amount of -mass in shrinking tubes about unit-length geodesics. The proof uses bilinear oscillatory integral estimates of Lee …
We provide a necessary and sufficient condition that -norms, , of eigenfunctions of the square root of minus the Laplacian on 2-dimensional compact boundaryless Riemannian manifolds are small compared to a natural power of the eigenvalue . The condition that ensures this is that their norms ove…
If is a compact Riemannian manifold of dimension we give necessary and sufficient conditions for improved -norms of eigenfunctions for all , the critical exponent. Since improved bounds imply improvement all other exponents, these conditions are nece…
Recently, matrix norm has been widely applied to many areas such as computer vision, pattern recognition, biological study and etc. As an extension of vector norm, the mixed matrix norm is often used to find jointly sparse solutions. Moreover, an efficient iterative algorithm has been designed…
Example surfaces with curvature bounds but no Laplacian eigenvalue lower bound.
We refine and generalize several interpolation inequalities bounding the norm of a probability density with respect to the reference measure by its Sobolev norm and the Kantorovich distance to on a smooth weighted Riemannian manifold satisfying condition.
This study extends verifiable learning to boosted tree ensembles, enabling efficient security verification.
Let be an -dimensional complete Riemannian manifold with harmonic curvature and positive Yamabe constant. Denote by and the scalar curvature and the trace-free Riemannian curvature tensor of , respectively. The main result of this paper states that goes to ze…
The paper proves that under certain conditions, solutions to a specific differential inequality are nonnegative.
We prove an analogue of Sogge's local estimates for norms of restrictions of eigenfunctions to submanifolds, and use it to show that for quantum ergodic eigenfunctions one can get improvements of the results of Burq-Gérard-Tzvetkov, Hu, and Chen-Sogge. The improvements are logarithmic on negatively curved m…
In recent years several adversarial attacks and defenses have been proposed. Often seemingly robust models turn out to be non-robust when more sophisticated attacks are used. One way out of this dilemma are provable robustness guarantees. While provably robust models for specific -perturbation models have been dev…
Deep neural networks (DNNs) have recently achieved state-of-the-art performance and provide significant progress in many machine learning tasks, such as image classification, speech processing, natural language processing, etc. However, recent studies have shown that DNNs are vulnerable to adversarial attacks. For inst…
Local norms of Fourier multipliers bounded on discrete subgroups of Lie groups.
The evaluation of robustness against adversarial manipulation of neural networks-based classifiers is mainly tested with empirical attacks as methods for the exact computation, even when available, do not scale to large networks. We propose in this paper a new white-box adversarial attack wrt the -norms for $p \in…
Investors who optimize their portfolios under any of the coherent risk measures are naturally led to regularized portfolio optimization when they take into account the impact their trades make on the market. We show here that the impact function determines which regularizer is used. We also show that any regularizer ba…
We prove that for a so-called sticky process there exists an equivalent probability and a -martingale that is arbitrarily close to in norm. For continuous , can be chosen arbitrarily close to in supremum norm. In the case where is a local martingale we may choo…
We derive the mapping between two of the most pervasive utility functions, the mean square error () and the concordance correlation coefficient (CCC, ). Despite its drawbacks, is one of the most popular performance metrics (and a loss function); along with lately in many of the sequence prediction…
The paper improves curvature estimates for Ricci flow solutions with bounded scalar curvature.
Extends fractional uncertainty principles with extremizers and stability results.
The abstract discusses a new type of space and its properties.
Paper shows deep neural networks can approximate Korobov functions nearly optimally.
We explore the evolution of daily returns of four major US stock market indices during the technology crash of 2000, and the financial crisis of 2007-2009. Our methodology is based on topological data analysis (TDA). We use persistence homology to detect and quantify topological patterns that appear in multidimensional…
Global approximation for piecewise linear paths via signatures.
Study explores robust Orlicz spaces in finance, showing separability implications.
By using an explicit Bellman function, we prove a bilinear embedding theorem for the Laplacian associated with a weighted Riemannian manifold having the Bakry-Emery curvature bounded from below. The embedding, acting on the cartesian product of and , , involves estimates…
The Willmore conjecture states that any immersion F:T^2 -> R^n of a 2-torus into flat euclidean space satisfies . We prove it under the condition that the L^p-norm of the Gaussian curvature is sufficiently small.
We give an overview of the generalized Calderón-Zygmund theory for "non-integral" singular operators, that is, operators without kernels bounds but appropriate off-diagonal estimates. This theory is powerful enough to obtain weighted estimates for such operators and their commutators with $\BMO$ functions. of…
Smooth low-regular connections lead to smooth immersions with controlled regularity.
Assuming a lower bound on the Ricci curvature of a complete Riemannian manifold, for we show the existence of bounds on the local norm of the Ricci curvature that depend only on the dimension and which improve with volume collapse.