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arXiv research

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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3876114152 · Jun 202019922001200920172026
48 results for zero-mean activations

Randomly initialized wide neural networks with zero-mean activations are nearly independent, potentially solving AI interpretability limits.

problem Measuring the limits of AI interpretability.
method Randomly initialized neural networks with large width and zero-mean activation functions.
result Neural networks with zero-mean activations are nearly independent, solving the computational no-coincidence conjecture.

Deep Learning has revolutionized vision via convolutional neural networks (CNNs) and natural language processing via recurrent neural networks (RNNs). However, success stories of Deep Learning with standard feed-forward neural networks (FNNs) are rare. FNNs that perform well are typically shallow and, therefore cannot …

2017-06-08abs ↗pdf ↗

FF algorithm uses goodness as a measure of input quality, derived from likelihood-ratio tests.

problem Training each layer locally with a goodness measure.
method FF algorithm uses a likelihood-ratio test to define goodness, which is the sum of squared activations normalized between layers.
result The goodness measure is a sufficient statistic for a likelihood-ratio test, explaining the FF algorithm's performance.

Study duality of zero mean curvature surfaces in Heisenberg group.

problem Understanding the duality of zero mean curvature surfaces in the Lorentzian Heisenberg group.
method Investigation of a transformation surface associated with zero mean curvature surfaces in the Heisenberg group under two metrics.
result Derivation of the Sym formula for the dual surface in both metric cases.

Solves surface problem in 3D light cone.

problem Björling problem for zero mean curvature surfaces in the three-dimensional light cone.
method Solves the Björling problem for zero mean curvature surfaces in the three-dimensional light cone.
result Constructs and classifies all rotational zero mean curvature surfaces.

Study classifies zero mean curvature surfaces with planar curvature lines.

problem Characterizing surfaces with specific curvature properties.
method Complete classification and investigation of their relationship to Thomsen-type surfaces.
result Zero mean curvature surfaces with planar curvature lines belong to a 1-parameter family.

Paper investigates reflection principles for zero mean curvature surfaces in isotropic 3-space.

problem Investigating reflection principles for zero mean curvature surfaces in isotropic 3-space.
method Analyzes reflection principles for zero mean curvature surfaces in I3\mathbb{I}^3.
result Shows a reflection principle for isotropic line segments on zero mean curvature surfaces in I3\mathbb{I}^3.

LCW reduces activation shift in neural networks, improving training efficiency and generalization.

problem Activation shift in neural networks leading to non-zero mean preactivation values.
method Linearly constrained weights (LCW) to reduce activation shift in fully connected and convolutional layers.
result LCW resolves the vanishing gradient problem and improves generalization of neural networks.

On any timelike surface with zero mean curvature in the four-dimensional Minkowski space we introduce special geometric (canonical) parameters and prove that the Gauss curvature and the normal curvature of the surface satisfy a system of two natural partial differential equations. Conversely, any two solutions to this …

2011-11-18abs ↗pdf ↗

New examples of mixed-type zero-curvature graphs found.

problem Finding new examples of zero-curvature graphs in Lorentz-Minkowski space.
method Using Konderak's representation formula to construct entire zero-curvature graphs over specific planes.
result Existence of new types of entire zero-curvature graphs in mixed-type in Lorentz-Minkowski space.

Riemann zero mean curvature examples in the Lorentz-Minkowski space are surfaces with zero mean curvature foliated by circles contained in parallel planes. In contrast to the Euclidean case, this family of surfaces presents new and rich features because of the variety of types of circles. In this paper, we give a geome…

2018-12-03abs ↗pdf ↗

The paper shows how Scherk-type surfaces can be decomposed into helicoids.

problem Decomposing Scherk-type zero mean curvature surfaces.
method Using a special Euler-Ramanujan identity and Wick rotation, the paper expresses these surfaces as an infinite superposition of dilated helicoids and provides different finite decompositions.
result Scherk-type zero mean curvature surfaces can be expressed as an infinite superposition of dilated helicoids.

Batch normalization (BN) is a technique to normalize activations in intermediate layers of deep neural networks. Its tendency to improve accuracy and speed up training have established BN as a favorite technique in deep learning. Yet, despite its enormous success, there remains little consensus on the exact reason and …

2018-06-01abs ↗pdf ↗

Study maxfaces and minfaces converging to surfaces with folded singularities.

problem Analyzing surfaces with folded singularities and their convergence properties.
method Constructing families of maxfaces and minfaces with increasing cuspidal crosscaps.
result Maxfaces and minfaces converge to surfaces with folded singularities.

Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.

problem Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.
method Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.
result Derive new Euler-Ramanujan-type identities and infinite decompositions for zero mean curvature graphs in various spaces.

The class of second order ODE's cubic with respect to the first order derivative is considered. Using geometric structures associated with these equations, the subclasses of umbilical equations, zero mean curvature equations, and zero Gaussian curvature equations are defined. Zero mean curvature equations are studied w…

2017-05-18abs ↗pdf ↗

Study on surfaces in neutral space forms with zero mean curvature.

problem Characterizing surfaces with zero mean curvature in neutral space forms.
method Analyzing curvature and normal connection properties of time-like conformal immersions.
result Conditions for surfaces with zero mean curvature in neutral space forms.

In this paper we provide a family of algebraic space-like surfaces in the three dimensional anti de Sitter space that shows that this Lorentzian manifold admits algebraic maximal examples of any order. Then, we classify all the space-like order two algebraic maximal hypersurfaces in the anti de Sitter NN-dimensional s…

2009-03-13abs ↗pdf ↗

Ordinary stochastic neural networks mostly rely on the expected values of their weights to make predictions, whereas the induced noise is mostly used to capture the uncertainty, prevent overfitting and slightly boost the performance through test-time averaging. In this paper, we introduce variance layers, a different k…

2018-03-10abs ↗pdf ↗

FF algorithm uses goodness as a likelihood-ratio test for scalar normalization.

problem Training each layer locally with scalar goodness.
method FF algorithm uses a likelihood-ratio test with squared goodness as the sufficient statistic.
result The FF algorithm generalizes to anisotropic and heavy-tailed populations.

Paper relaxes independence assumption for non-centered data.

problem Failing to account for dependencies in data leads to model failures.
method Proposes 'Kronecker-sum-structured mean' assumption to relax zero-mean requirement.
result Models with nonconvex but unimodal log-likelihoods can be solved efficiently.

Study Born-Infeld solitons and solve Björling problem for them.

problem Existence and non-uniqueness of solutions to the Björling problem for Born-Infeld solitons.
method Two approaches: treating as time-like minimal surfaces or using Barbashov-Chernikov representation.
result Solution to Björling problem may not be unique.

The paper proves a Moser-Trudinger inequality for zero-mean functions in 2D.

problem Proving a Moser-Trudinger inequality for zero-mean functions in 2D.
method Analyzing the supremum of a specific integral over functions in W1,2(Ω)W^{1,2}(Ω) with zero mean and bounded gradient norm.
result The supremum is finite and can be attained for β(0,1)β \in (0,1), partially generalizing Chang and Yang's result.

Gradient descent learns a single neuron without knowing the relationship between inputs and labels.

problem Learning a single neuron without knowing the relationship between inputs and labels.
method Using gradient descent to minimize empirical risk over i.i.d. samples, with a nonconvex and nonsmooth optimization problem.
result Gradient descent achieves near-optimal population risk in polynomial time and sample complexity.

New connections found on zero-mean multivariate normal distributions.

problem Characterizing statistical connections on zero-mean multivariate normal distributions.
method Investigating invariant conjugate symmetric statistical connections on the submanifold of zero-mean multivariate normal distributions.
result Invariant connections on zero-mean multivariate normal distributions are not uniquely characterized by invariance under the general linear group action.

Study shows zero level sets of solutions to Allen-Cahn equation are minimal surfaces with zero mean curvature.

problem Understanding phase transitions through entire solutions of the Allen-Cahn equation.
method Proving minimality of the zero level set with respect to a perimeter functional with density and showing zero mean curvature.
result The zero level set of entire solutions of the Allen-Cahn equation has zero mean curvature and is minimal.

Improved bound for Gaussian mechanism in differential privacy.

problem Finding tighter bounds for Gaussian mechanism in differential privacy.
method Presented a new closed form bound for (ε,δ)(ε, δ)-differential privacy using zero mean Gaussian noise.
result The new bound is always lower and valid for all ε>0ε > 0.

This paper explains how batch normalization auto-tunes the regularization parameter based on data statistics.

problem Batch normalization accelerates deep learning training but the exact relationship to regularization is unclear.
method Theoretical analysis and empirical validation of batch normalization's role in auto-tuning the regularization parameter.
result Batch normalization auto-tunes the regularization parameter based on data statistics.