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Statistical inferences for high-dimensional regression models have been extensively studied for their wide applications ranging from genomics, neuroscience, to economics. However, in practice, there are often potential unmeasured…

Methodology · Statistics 2023-09-12 Jing Ouyang , Kean Ming Tan , Gongjun Xu

We construct integrability-preserving deformations of the integrable $\sigma$-model coupling together $N$ copies of the Principal Chiral Model. These deformed theories are obtained using the formalism of affine Gaudin models, by applying…

High Energy Physics - Theory · Physics 2020-05-19 Cristian Bassi , Sylvain Lacroix

A Generalised Hadamard Transform for multi-phase or multilevel signals is introduced, which includes the Fourier, Generalised, Discrete Fourier, Walsh-Hadamard and Reverse Jacket Transforms. The jacket construction is formalised and shown…

Information Theory · Computer Science 2016-11-17 K. J. Horadam

In this PhD thesis we review some aspects of integrable models related to string backgrounds or their deformations. In the first part we develop methods to obtain exact results in the AdS3/CFT2 correspondence. We consider the AdS_3 x S^3 x…

High Energy Physics - Theory · Physics 2016-05-11 Riccardo Borsato

This contribution extends linear models for feature vectors to sublinear models for graphs and analyzes their properties. The results are (i) a geometric interpretation of sublinear classifiers, (ii) a generic learning rule based on the…

Machine Learning · Computer Science 2014-03-11 Brijnesh J. Jain

Recently, Chach\'olski, Neeman, Pitsch, and Scherer studied, in a series of three papers, model approximations for the unbounded category of cochain complexes over a commutative ring. These approximations allow to construct relative…

K-Theory and Homology · Mathematics 2015-03-31 Simone Virili

We consider a higher-derivative generalization of disformal transformations in $D$-dimensional spacetime and clarify the conditions under which they form a group with respect to the matrix product and the functional composition. These…

General Relativity and Quantum Cosmology · Physics 2022-01-06 Kazufumi Takahashi , Hayato Motohashi , Masato Minamitsuji

We present a computational and statistical approach for fitting isotonic models under convex differentiable loss functions. We offer a recursive partitioning algorithm which provably and efficiently solves isotonic regression under any such…

Methodology · Statistics 2012-10-09 Ronny Luss , Saharon Rosset

Bayesian inference for graphical models has received much attention in the literature in recent years. It is well known that when the graph G is decomposable, Bayesian inference is significantly more tractable than in the general…

Methodology · Statistics 2015-05-05 Kshitij Khare , Bala Rajaratnam , Abhishek Saha

With the apparent discovery of the Higgs boson, the Standard Model has been confirmed as the theory accounting for all sub-atomic phenomena. This observation lends further credence to the perturbative unification in Grand Unified Theories…

High Energy Physics - Theory · Physics 2014-03-26 Laura Bernard , Alon E Faraggi , Ivan Glasser , John Rizos , Hasan Sonmez

Numerous significant progress on fisheye image rectification has been achieved through CNN. Nevertheless, constrained by a fixed receptive field, the global distribution and the local symmetry of the distortion have not been fully…

Computer Vision and Pattern Recognition · Computer Science 2022-08-02 Shangrong Yang , Chunyu Lin , Kang Liao , Yao Zhao

I train a Generative Adversarial Network to produce realistic seismic wave speed models. I integrate the generator network into seismic Full-Waveform Inversion to reduce the number of model parameters and restrict the inverted models to…

Geophysics · Physics 2018-06-05 Alan Richardson

We study bilinear embedding models for the task of multi-relational link prediction and knowledge graph completion. Bilinear models belong to the most basic models for this task, they are comparably efficient to train and use, and they can…

Machine Learning · Computer Science 2017-09-15 Yanjie Wang , Rainer Gemulla , Hui Li

Dimer models are 2-dimensional combinatorial systems that have been shown to encode the gauge groups, matter content and tree-level superpotential of the world-volume quiver gauge theories obtained by placing D3-branes at the tip of a…

High Energy Physics - Theory · Physics 2008-09-17 Bo Feng , Yang-Hui He , Kristian D. Kennaway , Cumrun Vafa

We associate bicomplexes with several integrable models in such a way that conserved currents are obtained by a simple iterative construction. Gauge transformations and dressings are discussed in this framework and several examples are…

Exactly Solvable and Integrable Systems · Physics 2008-11-26 Aristophanes Dimakis , Folkert Muller-Hoissen

In this paper, we present a new class of invertible transformations with an application to flow-based generative models. We indicate that many well-known invertible transformations in reversible logic and reversible neural networks could be…

Machine Learning · Computer Science 2021-07-13 Jakub M. Tomczak

Decomposable dependency models and their graphical counterparts, i.e., chordal graphs, possess a number of interesting and useful properties. On the basis of two characterizations of decomposable models in terms of independence…

Artificial Intelligence · Computer Science 2013-02-08 Luis M. de Campos , Juan F. Huete

This is a survey article for "Handbook of Linear Algebra", 2nd ed., Chapman & Hall/CRC, 2014. An informal introduction to representations of quivers and finite dimensional algebras from a linear algebraist's point of view is given. The…

Representation Theory · Mathematics 2013-12-31 Roger A. Horn , Vladimir V. Sergeichuk

To a pair $(A,s)$ consisting of a smooth, cyclic $A_\infty$-algebra $A$ and a splitting $s$ of the Hodge filtration on its Hochschild homology Costello (2005) associates an invariant which conjecturally generalizes the total descendant…

Symplectic Geometry · Mathematics 2025-03-12 Andrei Caldararu , Junwu Tu

The diagonal of a model's Fisher Information Matrix (the "Fisher diagonal") has frequently been used as a way to measure parameter sensitivity. Typically, the Fisher diagonal is estimated via squared sampled gradients of the model's…

Machine Learning · Computer Science 2025-07-28 YuXin Li , Felix Dangel , Derek Tam , Colin Raffel
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