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We use a formal correspondence between thermodynamics and inference, where the number of samples can be thought of as the inverse temperature, to study a quantity called ``learning capacity'' which is a measure of the effective…

Machine Learning · Computer Science 2024-10-22 Daiwei Chen , Wei-Kai Chang , Pratik Chaudhari

The prime numbers and the non-trivial zeros of the Riemann zeta function are globally linked by the explicit formula of analytic number theory. Whether they share a hidden, scale-by-scale geometric symmetry has remained unexplored. We…

General Mathematics · Mathematics 2026-05-26 Zhengqiang Li

We formalize a transfinite Phi process that treats all possibility embeddings as operators on structured state spaces including complete lattices, Banach and Hilbert spaces, and orthomodular lattices. We prove a determinization lemma…

Functional Analysis · Mathematics 2025-08-15 Bugra Kilictas , Faruk Alpay

We consider a random Schr\"odinger operator on the binary tree with a random potential which is the sum of a random radially symmetric potential, $Q_r$, and a random transversally periodic potential, $\kappa Q_t$, with coupling constant…

Mathematical Physics · Physics 2018-01-03 Richard Froese , Darrick Lee , Christian Sadel , Wolfgang Spitzer , Günter Stolz

A fundamental aspect of limitations in learning any computation in neural architectures is characterizing their optimal capacities. An important, widely-used neural architecture is known as autoencoders where the network reconstructs the…

Neurons and Cognition · Quantitative Biology 2017-05-23 Alireza Alemi , Alia Abbara

The effective potential for the two-replica system of the random energy model is exactly derived. It is an analytic function of the magnetizations of two replicas, $\varphi^1$ and $\varphi^2$ in the high-temperature phase. In the…

Disordered Systems and Neural Networks · Physics 2016-01-06 Hisamitsu Mukaida

In this paper, we utilize the framework of Markov processes to attain a more probabilistic perspective on the theory of transfer operators. In doing so, we establish a functional central limit theorem (FLCT) for an $O(N)$ model associated…

Dynamical Systems · Mathematics 2023-10-23 Eduardo A. Silva , Elis G. Mesquita , Edgar Matias

Binary perceptron is a fundamental model of supervised learning for the non-convex optimization, which is a root of the popular deep learning. Binary perceptron is able to achieve a classification of random high-dimensional data by…

Disordered Systems and Neural Networks · Physics 2022-04-12 Yang Zhao , Junbin Qiu , Mingshan Xie , Haiping Huang

We derive the calculation of two critical numbers predicting the behavior of perceptron networks. First, we derive the calculation of what we call the lossless memory (LM) dimension. The LM dimension is a generalization of the…

Neural and Evolutionary Computing · Computer Science 2018-09-11 Gerald Friedland , Mario Krell

The axiomatic structure of the $\kappa$-statistcal theory is proven. In addition to the first three standard Khinchin--Shannon axioms of continuity, maximality, and expansibility, two further axioms are identified, namely the self-duality…

Statistical Mechanics · Physics 2024-05-14 G. Kaniadakis

We study the connected ensemble, a statistical-mechanics framework that characterizes the formation of low-loss paths in rugged landscapes. First introduced in a previous paper, this ensemble allows one to identify when a network can be…

Disordered Systems and Neural Networks · Physics 2026-02-02 Damien Barbier

The excess free energy functional of classical density functional theory depends upon the type of fluid model, specifically on the choice of (pair) potential, is unknown in general, and is approximated reliably only in special cases. We…

Soft Condensed Matter · Physics 2025-03-12 Stefanie M. Kampa , Florian Sammüller , Matthias Schmidt , Robert Evans

This paper extends our paper \cite{C2} for the conference ``Computability in Europe'' 2022. After Infinite Time Turing Machines (ITTM) were introduced in Hamkins and Lewis \cite{HL}, a number of machine models of computability have been…

Logic · Mathematics 2026-05-19 Merlin Carl

The weight space of the Ising perceptron in which a set of random patterns is stored is examined using the generating function of the partition function $\phi(n)=(1/N)\log [Z^n]$ as the dimension of the weight vector $N$ tends to infinity,…

Disordered Systems and Neural Networks · Physics 2015-05-14 Tomoyuki Obuchi , Yoshiyuki Kabashima

In the critical beta-splitting model of a random $n$-leaf binary tree, leaf-sets are recursively split into subsets, and a set of $m$ leaves is split into subsets containing $i$ and $m-i$ leaves with probabilities proportional to…

Probability · Mathematics 2024-09-09 David Aldous , Boris Pittel

In "Reliable Communication in the Absence of a Common Clock" (Yeung et al., 2009), the authors introduce general run-length sets, which form a class of constrained systems that permit run-lengths from a countably infinite set. For a…

Information Theory · Computer Science 2010-01-14 Georg Böcherer , Rudolf Mathar , Valdemar Cardoso da Rocha Junior , Cecilio Pimentel

We investigate the class of models of a general dependent theory. We continue math.LO/0702292 in particular investigating so called "decomposition of types"; thesis is that what holds for stable theory and for Th(Q,<) hold for dependent…

Logic · Mathematics 2012-02-28 Saharon Shelah

A stationary subset $S$ of a regular uncountable cardinal $\kappa$ {\it reflects fully} at regular cardinals if for every stationary set $T \subseteq \kappa$ of higher order consisting of regular cardinals there exists an $\alpha \in T$…

Logic · Mathematics 2008-02-03 Thomas Jech , Jiří Witzany

This note is devoted to exploring some analytic-geometric properties of the regularity and capacity associated to the so-called fractional dissipative operator $\partial_t+(-\Delta)^\alpha$, naturally establishing a diagonally sharp…

Analysis of PDEs · Mathematics 2013-01-03 Renjin Jiang , Jie Xiao , Dachun Yang , Zhichun Zhai

We introduce and study a notion of duality for two classes of optimization problems commonly occurring in probability theory. That is, on an abstract measurable space $(\Omega,\mathcal{F})$, we consider pairs $(E,\mathcal{G})$ where $E$ is…

Probability · Mathematics 2025-07-03 Adam Quinn Jaffe