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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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6481,2971,9452,593 · Jun 202019922001200920172026
48 results for widths of links

Khovanov homology is a bigraded Z-module that categorifies the Jones polynomial. The support of Khovanov homology lies on a finite number of slope two lines with respect to the bigrading. The Khovanov width is essentially the largest horizontal distance between two such lines. We show that it is possible to generate in…

2009-01-15abs ↗pdf ↗

There are many "minimax" complexity functions in mathematics: width of a tree or a link, Heegaard genus of a 3-manifold, the Cheeger constant of a Riemannian manifold. We define such a function w, "width", on countable (or finite) groups and show w(Z^k) = k-1.

2010-11-10abs ↗pdf ↗

We give asymptotically sharp upper bounds for the Khovanov width and the dealternation number of positive braid links, in terms of their crossing number. The same braid-theoretic technique, combined with Ozsváth, Stipsicz, and Szabó's Upsilon invariant, allows us to determine the exact cobordism distance between torus …

2016-10-14abs ↗pdf ↗

Wu has shown that if a link or a knot LL in S3S^3 in thin position has thin spheres, then the thin sphere of lowest width is an essential surface in the link complement. In this paper we show that if we further assume that LS3L \subset S^3 is prime, then the thin sphere of lowest width also does not have any vertical c…

2008-01-12abs ↗pdf ↗

We give a general fixed parameter tractable algorithm to compute quantum invariants of links presented by diagrams, whose complexity is singly exponential in the carving-width (or the tree-width) of the diagram. In particular, we get a O(N32cwpoly(n))O(N^{\frac{3}{2} \mathrm{cw}} \mathrm{poly}(n)) time algorithm to compute any Resh…

2019-10-01abs ↗pdf ↗

The paper provides examples of keen weakly reducible bridge spheres for links in b-bridge position.

problem Characterizing and finding examples of keen weakly reducible bridge spheres.
method Analyzing bridge spheres and their properties in terms of compressing disks and width complex.
result Infinitely many examples of keen weakly reducible bridge spheres for links in b-bridge position.

The family of negative torus links Tp,qT_{p,q} over a fixed number of strands pp admits a stable limit in reduced Khovanov homology as qq grows to infinity. In this paper, we endow this stable space with a bi-graded commutative algebra structure. We describe these algebras explicitly for p=2,3,4p=2,3,4. As an application, w…

2017-06-27abs ↗pdf ↗

For a 3-manifold with torus boundary admitting an appropriate involution, we show that Khovanov homology provides obstructions to certain exceptional Dehn fillings. For example, given a strongly invertible knot in S^3, we give obstructions to lens space surgeries, as well as obstructions to surgeries with finite fundam…

2008-07-09abs ↗pdf ↗

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.

We introduce the Mondrian kernel, a fast random feature approximation to the Laplace kernel. It is suitable for both batch and online learning, and admits a fast kernel-width-selection procedure as the random features can be re-used efficiently for all kernel widths. The features are constructed by sampling trees via a…

2016-06-16abs ↗pdf ↗

New framework connects two neural network theories, improving finite-width approximations.

problem Theoretical guarantees for neural network training in general cases.
method Developed a general framework linking mean-field and constant kernel theories.
result Discrete-time MF limit provides better approximation for finite-width nets.

We give the rectangle condition for strong irreducibility of Heegaard splittings of 33-manifolds with non-empty boundary. We apply this to a generalized Heegaard splitting of a 22-fold covering of S3S^3 branched along a link. The condition implies that any thin meridional level surface in the link complement is incom…

2010-07-15abs ↗pdf ↗

Semi-supervised clustering aims to introduce prior knowledge in the decision process of a clustering algorithm. In this paper, we propose a novel semi-supervised clustering algorithm based on the information-maximization principle. The proposed method is an extension of a previous unsupervised information-maximization …

2013-04-30abs ↗pdf ↗

Lobb observed in [arXiv:1103.1412] that each equivariant sl(N) Khovanov-Rozansky homology over C[a] admits a standard decomposition of a simple form. In the present paper, we derive a formula for the corresponding Lee-Gornik spectral sequence in terms of this decomposition. Based on this formula, we give a simple alter…

2012-11-28abs ↗pdf ↗

Bayesian deep ensembles improve prediction accuracy in various settings.

problem Improving prediction accuracy of deep ensembles in out-of-distribution settings.
method Introducing a randomised, untrainable function to each ensemble member, enabling a posterior predictive distribution interpretation.
result Bayesian deep ensembles make more conservative predictions and outperform standard ensembles in various tasks.

Every knot has a plat projection, obtained by closing up a braid with bridges. The plat projection is determined by the number of strands and the number of rows of twist regions in the braid, and an integer number of crossings in each twist region. In recent work, we showed that under certain restrictions, including th…

2018-03-02abs ↗pdf ↗

Better uncertainty estimates for neural networks using Gaussian process priors.

problem Poor uncertainty estimates in neural networks, especially on out-of-distribution data.
method Characterize the function-space prior of an ensemble of infinitely-wide neural networks as a Gaussian process and use it to build a probabilistic model.
result The approach improves calibration of neural networks, especially under distributional shift.

We define the Wirtinger width of a knot. Then we prove the Wirtinger width of a knot equals its Gabai width. The algorithmic nature of the Wirtinger width leads to an efficient technique for establishing upper bounds on Gabai width. As an application, we use this technique to calculate the Gabai width of approximately …

2019-12-04abs ↗pdf ↗

Empirical study compares finite- and infinite-width BNNs, revealing performance differences under model mismatch.

problem Comparing BNNs with different widths due to conflicting model properties and inference intractability.
method Empirical comparison of finite- and infinite-width BNNs, analyzing performance under model mismatch.
result Increasing width can hurt BNN performance when the model is mis-specified, and finite-width BNNs generalize better under model mismatch.

Sharp asymptotics reveal how network width controls learnability in quadratic neural networks.

problem Understanding learnability in overparameterized quadratic neural networks.
method Mapping ERM to convex matrix sensing with nuclear norm penalization.
result Characterization of global minima and precise generalization thresholds.

We study deep neural networks with polynomial activations, particularly their expressive power. For a fixed architecture and activation degree, a polynomial neural network defines an algebraic map from weights to polynomials. The image of this map is the functional space associated to the network, and it is an irreduci…

2019-05-29abs ↗pdf ↗

Lectures on deep learning properties in infinite and large-width networks.

problem Understanding deep neural networks in extreme width conditions.
method Analysis of random deep neural networks, connections to linear models, kernels, and Gaussian processes, perturbative and non-perturbative treatments.
result Properties and behaviors of deep neural networks in the infinite-width limit and large-width regime.

A ribbon is, intuitively, a smooth mapping of an annulus S1×IS^1 \times I in 3-space having constant width ε\varepsilon. This can be formalized as a triple (x,ε,u)(x,\varepsilon, \mathbf{u}) where xx is smooth curve in 3-space and u\mathbf{u} is a unit vector field based along xx. In the 1960s and 1970s, G. Calugareanu, G…

2018-08-01abs ↗pdf ↗

A number of results for C2^2-smooth surfaces of constant width in Euclidean 3-space E3{\mathbb{E}}^3 are obtained. In particular, an integral inequality for constant width surfaces is established. This is used to prove that the ratio of volume to cubed width of a constant width surface is reduced by shrinking it along…

2007-04-24abs ↗pdf ↗

We define two new families of invariants for (3-manifold, graph) pairs which detect the unknot and are additive under connected sum of pairs and (-1/2)-additive under trivalent vertex sum of pairs. The first of these families is closely related to both bridge number and tunnel number. The second of these families is a …

2016-06-10abs ↗pdf ↗

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.

We define a new notion of thin position for a graph in a 3-manifold which combines the ideas of thin position for manifolds first originated by Scharlemann and Thompson with the idea of thin position for knots first originated by Gabai. This thin position has the property that connect summing annuli and pairs-of-pants …

2016-06-10abs ↗pdf ↗

While studying the existence of closed geodesics and minimal hypersurfaces in compact manifolds, the concept of width was introduced in different contexts. Generally, the width is realized by the energy of the closed geodesics or the volume of minimal hypersurfaces, which are found by the Minimax argument. Recently, Ma…

2016-12-20abs ↗pdf ↗

Study infinite-depth limits of neural networks with fixed width.

problem Understanding the behavior of neural networks as depth increases with fixed width.
method Analyzing finite-width residual networks with random Gaussian weights, focusing on the infinite-depth limit.
result The pre-activations converge to a zero-drift diffusion process, differing from the infinite-width limit.

Study on ribbonlength and crossing number for folded ribbon knots.

problem Understanding the relationship between ribbonlength and crossing number for folded ribbon knots.
method Used two different methods to establish upper bounds on ribbonlength in terms of crossing number.
result Found constants c1c_1 and c2c_2 such that ribbonlength is bounded above by c1Cr(K)2c_1\cdot Cr(K)^2 and c2Cr(K)3/2c_2\cdot Cr(K)^{3/2}, respectively.