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We present a self-contained analysis of theories of discrete 2D gravity coupled to matter, using geometric methods to derive equations for generating functions in terms of free (noncommuting) variables. For the class of discrete gravity…

High Energy Physics - Theory · Physics 2008-11-26 Sean M. Carroll , Miguel E. Ortiz , Washington Taylor

In machine learning, stochastic gradient descent (SGD) is widely deployed to train models using highly non-convex objectives with equally complex noise models. Unfortunately, SGD theory often makes restrictive assumptions that fail to…

Machine Learning · Computer Science 2022-10-11 Vivak Patel , Shushu Zhang , Bowen Tian

The hypothesis of a discrete fabric of the universe--the "Planck scale"--is always on stage, since it solves mathematical and conceptual problems in the infinitely small. However, it clashes with special relativity, which is designed for…

Quantum Physics · Physics 2016-10-26 Alessandro Bisio , Giacomo Mauro D'Ariano , Paolo Perinotti

In several experimental reports on nonconvex optimization problems in machine learning, stochastic gradient descent (SGD) was observed to prefer minimizers with flat basins in comparison to more deterministic methods, yet there is very…

Optimization and Control · Mathematics 2018-05-08 Vivak Patel

We classify symmetric backgrounds of eleven-dimensional supergravity up to local isometry. In other words, we classify triples (M,g,F), where (M,g) is an eleven-dimensional lorentzian locally symmetric space and F is an invariant 4-form,…

High Energy Physics - Theory · Physics 2015-06-03 José Figueroa-O'Farrill

Stochastic gradient descent (SGD) is widely used in machine learning. Although being commonly viewed as a fast but not accurate version of gradient descent (GD), it always finds better solutions than GD for modern neural networks. In order…

Machine Learning · Computer Science 2018-08-17 Robert Kleinberg , Yuanzhi Li , Yang Yuan

Astronomical observations reveal a major deficiency in our understanding of physics $-$ the detectable mass is insufficient to explain the observed motions in a huge variety of systems given our current understanding of gravity, Einstein's…

Cosmology and Nongalactic Astrophysics · Physics 2022-07-07 Indranil Banik , Hongsheng Zhao

We propose that there is a regime of quantum gravity phenomena, for the case that the cosmological constant is small and positive, which concerns phenomena at temperatures below the deSitter temperature, or length scales larger than the…

General Relativity and Quantum Cosmology · Physics 2017-11-01 Lee Smolin

Generalized complex geometry is an example of a powerful formalism to attempt the construction of a language adequate to string theory. With the remarkable property of unifying symplectic and complex manifolds as special cases of a broader…

High Energy Physics - Theory · Physics 2013-03-26 Sara Oriana Tavares

Algebras of generalized functions offer possibilities beyond the purely distributional approach in modelling singular quantities in non-smooth differential geometry. This article presents an introductory survey of recent developments in…

Functional Analysis · Mathematics 2007-05-23 Michael Kunzinger

We develop new sub-optimality bounds for gradient descent (GD) that depend on the conditioning of the objective along the path of optimization rather than on global, worst-case constants. Key to our proofs is directional smoothness, a…

Machine Learning · Computer Science 2025-01-15 Aaron Mishkin , Ahmed Khaled , Yuanhao Wang , Aaron Defazio , Robert M. Gower

The formalism to determine (conformal) isometries of a given curved superspace was elaborated almost two decades ago in the context of the old minimal formulation for N=1 supergravity in four dimensions (4D). This formalism is universal,…

High Energy Physics - Theory · Physics 2015-06-12 Sergei M. Kuzenko

The topos theory is a theory which is used for deciding a number of problems of theory of relativity, gravitation and quantum physics. It is known that topos-theoretic geometry can be successfully developed within the framework of Synthetic…

General Physics · Physics 2007-05-23 Alexander K. Guts , Artem A. Zvyagintsev

A vertex $v\in V$ is said to resolve two vertices $x$ and $y$ if $d_G(v,x)\ne d_G(v,y)$. A set $S\subset V$ is said to be a metric generator for $G$ if any pair of vertices of $G$ is resolved by some element of $S$. A minimum metric…

Combinatorics · Mathematics 2017-04-25 Y. Ramirez-Cruz , O. R. Oellermann , J. A. Rodriguez-Velazquez

It was shown recently that stochastic quantization can be made into a well defined quantization scheme on (pseudo-)Riemannian manifolds using second order differential geometry, which is an extension of the commonly used first order…

General Relativity and Quantum Cosmology · Physics 2021-12-28 Folkert Kuipers

Phenomenological approaches to quantum gravity implement a minimum resolvable length-scale but do not link it to an underlying formalism describing geometric superpositions. Here, we introduce an intuitive approach in which points in the…

Quantum Physics · Physics 2019-09-05 Matthew J. Lake , Marek Miller , Ray F. Ganardi , Zheng Liu , Shi-Dong Liang , Tomasz Paterek

The modified newtonian dynamics (MOND) paradigm of Milgrom can boast of a number of successful predictions regarding galactic dynamics; these are made without the assumption that dark matter plays a significant role. MOND requires…

Astrophysics · Physics 2014-10-13 Jacob D. Bekenstein

Gravity is understood as a geometrization of spacetime. But spacetime is also the manifold of the boundary values of the spinless point particle in a variational approach. Since all known matter, baryons, leptons and gauge bosons are…

General Relativity and Quantum Cosmology · Physics 2015-06-04 Martin Rivas

We describe gauge theories which allow to retrieve a large class of gravitational theories, including, MacDowell-Mansouri gravity and its topological extension to Loop Quantum Gravity via the Pontrjagin characteristic class involving the…

General Relativity and Quantum Cosmology · Physics 2026-01-07 J. Thibaut , S. Lazzarini

The geometry of generative models serves as the basis for interpolation, model inspection, and more. Unfortunately, most generative models lack a principal notion of geometry without restrictive assumptions on either the model or the data…

Machine Learning · Computer Science 2026-01-30 Frederik Möbius Rygaard , Shen Zhu , Yinzhu Jin , Søren Hauberg , Tom Fletcher
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