Sharp lower bound for first Neumann eigenvalue found in terms of diameter and width.
problem Finding the minimum value of the first Neumann eigenvalue for convex domains.
method Proved the sharp lower bound using diameter and width.
result Sharp lower bound for the first Neumann eigenvalue established.
Study shows upper limit for torical band width with spectral curvature bounds.
problem Understanding the band width of torical bands with spectral curvature constraints.
method Used the warped \( μ\)-bubble method with spectral curvature bounds.
result Upper bound for the band width of torical bands is established.
Study bounds Urysohn width of manifolds under surgeries.
problem Bounding Urysohn width of manifolds after surgeries.
method Analyzes connected sums and universal covers, applies to general surgeries.
result Optimal constants in estimates of width bounds are shown.
Riemannian manifolds with bounded Ricci curvature have finite Uryson width.
problem Bounding Uryson width for manifolds with Ricci curvature constraints.
method Continuous map to a polyhedral space with controlled diameter.
result Riemannian manifolds with specific Ricci curvature bounds have finite Uryson width.
An algorithm calculates Gabai width for thousands of knots.
problem Calculating Gabai width for many knots.
method Algorithmic definition of Wirtinger width leading to efficient Gabai width bounds.
result Proved Wirtinger width equals Gabai width for knots.
The paper bounds the min-max width of embedded circles on spheres and manifolds.
problem Bounding the min-max width of embedded circles on spheres and manifolds.
method Inducing a sweepout by pairs of points in embedded circles from a given sweepout of the sphere by closed curves.
result Lower bounds for the Birkhoff min-max invariant of a Riemannian sphere in terms of the min-max width of its embedded circles.
Study shows bound on Uryson width for specific 3D manifolds.
problem Bounding Uryson width for 3D manifolds with non-negative Ricci curvature and strictly mean convex boundary.
method Proved existence of a Morse function with uniform diameter bounds on level sets.
result Upper bound on Uryson width for the specified 3D manifolds.
Sharp bounds on neural network approximation rates and widths.
problem Estimating approximation rates, metric entropy, and n-widths of shallow neural networks.
method Introducing smoothly parameterized dictionaries and providing upper and lower bounds.
result Sharp bounds on approximation rates, metric entropy, and n-widths for neural networks with various activation functions.
The paper proves finite Urysohn 1-width for 4 and 5 manifolds with positive biRicci curvature.
problem Proving finite Urysohn 1-width for 4 and 5 manifolds with positive biRicci curvature.
method Using positive biRicci curvature and uniform scalar curvature bounds, the paper shows that the Urysohn 1-width is finite and depends only on the curvature bounds.
result Closed 4 and 5 manifolds with positive biRicci curvature have finite Urysohn 1-width, which depends only on the curvature bounds.
Minimum width for ReLU networks to approximate L^p functions is max(d_x+1, d_y).
problem Characterizing the minimum width for ReLU networks to approximate L^p functions.
method Analyzing networks with ReLU activation functions and proving the minimum width required.
result The minimum width required for the universal approximation of L^p functions is exactly max(d_x+1, d_y).
Paper proves a noncompact version of Gromov's band-width estimate.
problem Proving a precise upper bound for noncompact Riemannian bands.
method Developed a quantitative partitioned manifold index theory.
result Proved a version of Gromov's band-width estimate for noncompact Riemannian bands.
Develops a new theory of width for embedded circles in Riemannian manifolds.
problem Defining and understanding the width of embedded circles in Riemannian manifolds.
method Morse-Lusternik-Schnirelmann theory applied to geodesics and minimising configurations.
result Classifies configurations of minimising geodesics intersecting embedded circles.
Improved bounds for neural network approximations of functions.
problem Bounding the width of neural networks for function approximation.
method Extending Radon-based norms to bounded open sets and deriving new approximation bounds.
result Improved sparse approximation bounds for neural networks.
Smooth manifolds can be triangulated with graphs of bounded twin-width.
problem Understanding the structure of triangulations of smooth manifolds.
method Using Whitney's triangulation method and bounding the twin-width of specific graphs.
result Compact smooth manifolds have triangulations with graphs of bounded twin-width.
Complex-valued neural networks can approximate any continuous function with bounded widths and depths.
problem Approximating continuous functions with complex-valued neural networks of bounded widths and depths.
method Analyzing activation functions and proving universality for complex-valued networks.
result Deep narrow complex-valued networks are universal if and only if their activation function is neither holomorphic, nor antiholomorphic, nor R \mathbb{R} R -affine. Establish optimal Lipschitz lower bounds for functions on manifolds with negative curvature, revealing interplay between width, boundary area, and topology.
problem Width estimates and rigidity of manifolds with negative curvature
method Gromov's μ-bubble method
result Sharp lower bound for boundary area in hyperbolic bands
Improved neural network depth-width trade-offs via dynamical systems.
problem Expressivity of neural networks in terms of depth and width.
method Connection with dynamical systems, focusing on periodic points and Lipschitz constants.
result Sharper width lower bounds for neural networks, yielding exponential depth-width separations.
Width trees link link invariants and bridge number.
problem Understanding link invariants through geometric structures.
method Associate width trees to links and use their geometric properties to bound link invariants.
result Width trees uniquely realize certain link invariants under specific conditions.
Chow and Liu (1968) studied the problem of learning a maximumlikelihood Markov tree. We generalize their work to more complexMarkov networks by considering the problem of learning a maximumlikelihood Markov network of bounded complexity. We discuss howtree-width is in many ways the appropriate measure of complexity and…
This research proves that two min-max theories for hypersurfaces are equivalent.
problem Comparing two min-max theories for hypersurfaces.
method Developed and proved the equivalence of Almgren-Pitts and Allen-Cahn min-max theories.
result The Almgren-Pitts widths and Allen-Cahn widths are equivalent.
Study Gaussian-process limits of neural networks using tensor programs.
problem Understanding the behavior of neural networks as they approach infinite width.
method Quantitative analysis through tensor programs and Wasserstein distance.
result Explicit finite-width error bounds, showing convergence to Gaussian-process limits.
Study on spherical bodies of constant width on the unit sphere, proving bounds on their relative effective radius.
problem Understanding the smallest spherical bodies of constant width on the unit sphere.
method Analyzing spherical bodies of constant width on the unit sphere, constructing examples and applying geometric arguments.
result Proved non-trivial bounds on the relative effective radius of spherical bodies of constant width.
New approach finds minimum width for deep, narrow MLPs.
problem Finding the minimum width for deep, narrow MLPs to approximate continuous functions.
method Proposes a framework to simplify finding minimum width into determining a geometrical function w ( d x , d y ) w(d_x, d_y) w ( d x , d y ) based on input and output dimensions. result Proves that w ( d x , d y ) w(d_x, d_y) w ( d x , d y ) equals the optimal minimum width for deep, narrow MLPs to achieve universality. This article concerns the expressive power of depth in neural nets with ReLU activations and bounded width. We are particularly interested in the following questions: what is the minimal width w min ( d ) w_{\text{min}}(d) w min ( d ) so that ReLU nets of width w min ( d ) w_{\text{min}}(d) w min ( d ) (and arbitrary depth) can approximate any continuous functio…
Fisher width is a geometric measure of complexity on statistical manifolds.
problem Complexity measures on statistical manifolds
method Introducing Fisher width as a Fisher-geometric analogue of Gaussian width
result Fisher width retains key structural features of Gaussian width while capturing anisotropic geometric effects
Study on ball widths and minimal submanifolds in space forms.
problem Understanding widths of balls and minimal submanifolds.
method Analyzing the area of equatorial balls and related bounds for minimal submanifolds.
result Lower bounds for the area of free boundary minimal submanifolds.
The paper proves unique geodesics on hyperbolic surfaces and finds lower bounds.
problem Characterizing geodesics on hyperbolic surfaces.
method One-parameter Allen-Cahn min-max constructions.
result Every geodesic occurs with multiplicity one and provides uniform sharp lower bounds.
Study on 1-Uryson width of polyhedra and their covers.
problem Existence of Riemannian polyhedra with bounded 1-Uryson width of covers but unbounded in the polyhedron itself.
method Investigated specific cases of virtually cyclic fundamental groups and Riemannian surfaces, showing bounds on 1-Uryson width.
result For compact polyhedra with virtually cyclic fundamental groups, 1-Uryson width of polyhedron is bounded by that of its universal cover.
2-width of 3-manifolds is bounded by 2 but embeddings can have infinite 2-width.
problem Defining and analyzing the 2-width of 3-manifolds and their embeddings.
method Generalizing width concept to 3-manifolds and embeddings, showing bounds and divergences.
result Embeddings of 3-manifolds can have arbitrarily large 2-width, challenging classical width concepts.
New findings show depth is more important than width in neural networks.
problem Understanding the role of width and depth in neural networks.
method Constructed networks with bounded weights and width at most d+2, showing depth plays a more significant role.
result Depth is more important than width in the expressive power of neural networks.
Quantitative CLTs show neural network distributions converge to Gaussian as width increases.
problem Understanding the distribution of fully connected neural networks with random weights and biases.
method Analyzing the distribution of a fully connected neural network with random Gaussian weights and biases, proving quantitative bounds on normal approximations.
result The distance between a random fully connected network and the corresponding infinite width Gaussian process scales like n − γ n^{-γ} n − γ for γ > 0 γ>0 γ > 0 . Sine activation functions enable two-layer neural networks to learn modular addition more efficiently.
problem Learning modular addition with two-layer neural networks.
method Introduced and analyzed sine activation functions, providing theoretical and empirical evidence.
result Sine activation functions allow for constant-width network realizations of modular addition, whereas ReLU networks require linear width scaling.
Graphs can't learn certain tasks due to depth vs width limitations.
problem Understanding limitations of graph neural networks in learning specific tasks.
method Analyzing expressive power of GNNmp under depth, width, and node attributes.
result GNNmp can lose significant power when depth and width are restricted.
Proves properties of neural network basins of attraction and their expressiveness.
problem Characterize the properties of basins of attraction in neural networks.
method Analyzes width-bounded neural networks, proving properties of basins of attraction.
result Boundedness and path-connectedness of basins of attraction under certain conditions.
New insights into p p p -widths of surfaces, proving optimality and calculating constants.
problem Understanding p p p -widths of surfaces and their relationship to geodesics. method Analyzing min-max sequences of minimal submanifolds and sweepouts of surfaces.
result Optimal sweepouts of the round two-sphere by ⌊ p f l o o r \lfloor \sqrt{p}
floor ⌊ p f l oor great circles. The paper sets limits on neural network sizes based on dataset shapes.
problem Understanding the size of neural networks needed for accurate predictions.
method Examined how the shape of data influences neural network complexity.
result Established upper limits on neural network width based on dataset topology.
Gradient methods improve deep network training with tighter bounds and faster convergence.
problem Improving convergence and generalization of gradient methods for neural networks.
method Algorithmic stability analysis and novel bounds on excess risk.
result Gradient descent achieves optimal excess risk for deep nets with polynomial width conditions.
We provide an upper bound for the Gromov width of compact homogeneous Hodge manifolds ( M , ω ) (M, ω) ( M , ω ) with b 2 ( M ) = 1 b_2(M)=1 b 2 ( M ) = 1 . As an application we obtain an upper bound on the Seshadri constant ε ( L ) ε(L) ε ( L ) where L L L is the ample line bundle on M M M such that c 1 ( L ) = [ ω π ] c_1(L)=[\fracωπ] c 1 ( L ) = [ π ω ] .
Study on width of Jordan curves in complex projective space, distinguishing quasicircles.
problem Characterizing Jordan curves in complex projective space by their width.
method Defining width in terms of hyperbolic geometry and analyzing convex hulls.
result Existence of Jordan curves of bounded width that are not quasicircles.
Residual networks with block width max(d_x, d_y) approximate all functions.
problem Achieving universal approximation with residual networks.
method Established bounds on block width for different activation functions.
result Minimum block width for universal approximation is max(d_x, d_y) with inner width 1.
New optimizers control network width scaling, improving stability and transfer across different model sizes.
problem Designing stable optimizers for networks of varying widths.
method Interpreting optimizers as steepest descent under mean-normalized operator norms, enabling layerwise composability and width-independent bounds.
result New optimizers like row normalization and column normalization provide stable learning-rate transfer across different model widths.
This paper tightens bounds on the smallest eigenvalue of NTK for deep ReLU networks.
problem Analyzing the smallest eigenvalue of Neural Tangent Kernel for deep ReLU networks.
method Analyzing various quantities of independent interest, including lower bounds on the smallest singular value of hidden feature matrices and upper bounds on the Lipschitz constant of input-output feature maps.
result Tight bounds on the smallest eigenvalue of NTK matrices for deep ReLU nets, both in the limiting case of infinite widths and for finite widths.
Study proves deep narrow RNNs can approximate any function, with minimum width independent of data length.
problem Proving universality of deep narrow RNNs with bounded widths.
method Analyzing RNNs as dynamical systems, proving universality for deep narrow structures with specific widths.
result Minimum width for universality of deep narrow RNNs is independent of data length.
Proves principles and estimates for initial data sets in Einstein equations.
problem Understanding initial data sets in Einstein equations.
method Spinorial Callias operator approach.
result Proves long neck principle and width estimates.
Uniform convergence of interpolators proven for Gaussian data.
problem Interpolation learning in high-dimensional linear regression with Gaussian data.
method Generic uniform convergence guarantee in terms of Gaussian width.
result Consistency of interpolators for minimum-norm and near-minimal-norm cases.
The study proves a tube theorem for complex hyperbolic manifolds.
problem Understanding the geometry of complex hyperbolic manifolds.
method Tubular neighborhood theorem and geometric combination theorem.
result Explicit estimates and bounds for tube widths in complex hyperbolic manifolds.
Study shows polynomial-width neural networks can closely approximate infinite-width networks in polynomial time.
problem Approximating dynamics of polynomial-width neural networks with infinite-width networks.
method Bounding approximation gap through a differential equation governed by mean-field dynamics, considering local Hessian.
result Polynomially many neurons are sufficient to closely approximate mean-field dynamics.
Deep neural networks' infinite-width behavior approximated by Gaussian models.
problem Understanding the behavior of deep neural networks in the limit of infinite width.
method Using the Lindeberg exchange principle to approximate weights by Gaussian random variables.
result Quantitative bounds on the 2-Wasserstein distance between deep neural networks and Gaussian limits.