Publications associées à Approximation of Pattern Transformation Manifolds with Parametric Dictionaries

Publications associées (29)

From low-rank retractions to dynamical low-rank approximation and back

Daniel Kressner, Axel Elie Joseph Séguin, Gianluca Ceruti

In algorithms for solving optimization problems constrained to a smooth manifold, retractions are a well-established tool to ensure that the iterates stay on the manifold. More recently, it has been demonstrated that retractions are a useful concept for ot ...

Springer2024

Floyd's manifold is a conjugation space

Jérôme Scherer

E. E. Floyd showed in 1973 that there exist only two nontrivial cobor-dism classes that contain manifolds with three cells, and that they lie in dimen-sions 10 and 5. We prove that there is an action of the cyclic group C2 on the 10-dimensional Floyd manif ...

Scuola Normale Superiore2023

Learning Transformations To Reduce the Geometric Shift in Object Detection

Mathieu Salzmann, Martin Pierre Engilberge, Vidit Vidit

The performance of modern object detectors drops when the test distribution differs from the training one. Most of the methods that address this focus on object appearance changes caused by, e.g., different illumination conditions, or gaps between syntheti ...

Ieee Computer Soc2023

Retraction-based numerical methods for continuation, interpolation and time integration on manifolds

Axel Elie Joseph Séguin

The goal of this thesis is the development and the analysis of numerical methods for problems where the unknown is a curve on a smooth manifold. In particular, the thesis is structured around the three following problems: homotopy continuation, curve inter ...

EPFL2023

Critical Schrodinger Cat Qubit

Vincenzo Savona, Fabrizio Minganti, Luca Gravina

Encoding quantum information onto bosonic systems is a promising route to quantum error correc-tion. In a cat code, this encoding relies on the confinement of the dynamics of the system onto the two-dimensional manifold spanned by Schrodinger cats of oppos ...

AMER PHYSICAL SOC2023

A structured prediction approach for robot imitation learning

Aude Billard, Iason Batzianoulis

We propose a structured prediction approach for robot imitation learning from demonstrations. Among various tools for robot imitation learning, supervised learning has been observed to have a prominent role. Structured prediction is a form of supervised le ...

Sage Publications Ltd2023

Synthesis of SFQ Circuits with Compound Gates

Giovanni De Micheli, Alessandro Tempia Calvino, Rassul Bairamkulov

Rapid single-flux quantum (RSFQ) is one of the most advanced superconducting technologies with the potential to supplement or replace conventional VLSI systems. However, scaling RSFQ systems up to VLSI complexity is challenging due to fundamental differenc ...

IEEE2023

Making Computer Vision Models Robust and Adaptive

Shuqing Teresa Yeo

Visual perception is indispensable for many real-world applications. However, perception models deployed in the real world will encounter numerous and unpredictable distribution shifts, for example, changes in geographic locations, motion blur, and adverse ...

EPFL2023

Continuous-variable neural network quantum states and the quantum rotor model

Giuseppe Carleo

We initiate the study of neural network quantum state algorithms for analyzing continuous-variable quantum systems in which the quantum degrees of freedom correspond to coordinates on a smooth manifold. A simple family of continuous-variable trial wavefunc ...

SPRINGERNATURE2023

Persistence and the Sheaf-Function Correspondence

Nicolas Michel Berkouk

The sheaf-function correspondence identifies the group of constructible functions on a real analytic manifold M with the Grothendieck group of constructible sheaves on M. When M is a finite dimensional real vector space, Kashiwara-Schapira have recently in ...

Cambridge Univ Press2023

A Theory of Finite-Width Neural Networks: Generalization, Scaling Laws, and the Loss Landscape

Berfin Simsek

Deep learning has achieved remarkable success in various challenging tasks such as generating images from natural language or engaging in lengthy conversations with humans. The success in practice stems from the ability to successfully train massive neural ...

EPFL2023

Finding stationary points on bounded-rank matrices: a geometric hurdle and a smooth remedy

Nicolas Boumal

We consider the problem of provably finding a stationary point of a smooth function to be minimized on the variety of bounded-rank matrices. This turns out to be unexpectedly delicate. We trace the difficulty back to a geometric obstacle: On a nonsmooth se ...

SPRINGER HEIDELBERG2022

A b-symplectic slice theorem

Anna Kiesenhofer

In this article, motivated by the study of symplectic structures on manifolds with boundary and the systematic study of b-symplectic manifolds started in Guillemin, Miranda, and Pires Adv. Math. 264 (2014), 864-896, we prove a slice theorem for Lie group a ...

WILEY2022

Self-Structuring Of Cellular And Channel Type In Complex System Dynamics In The Framework Of Scale Relativity Theory

Maria-Alexandra Paun

In the framework of Scale Relativity Theory, by analyzing dynamics of complex system structural units based on multifractal curves, both Schrodinger and Madelung approaches are functional and complementary. The Madelung selected approach involve synchronou ...

UNIV POLITEHNICA BUCHAREST, SCI BULL2022

Learning V1 Simple Cells with Vector Representation of Local Content and Matrix Representation of Local Motion

Yufan Ren

This paper proposes a representational model for image pairs such as consecutive video frames that are related by local pixel displacements, in the hope that the model may shed light on motion perception in primary visual cortex (V1). The model couples the ...

ASSOC ADVANCEMENT ARTIFICIAL INTELLIGENCE2022

An Accelerated First-Order Method for Non-convex Optimization on Manifolds

Christopher Arnold Criscitiello, Nicolas Boumal

We describe the first gradient methods on Riemannian manifolds to achieve accelerated rates in the non-convex case. Under Lipschitz assumptions on the Riemannian gradient and Hessian of the cost function, these methods find approximate first-order critical ...

SPRINGER2022

Exploring Photoredox-Catalyzed (Re)functionalizations with Core-Modified Benziodoxolones

Jérôme Waser, Franck Le Vaillant, Stephanie Grace Elizabeth Amos

Alkynes and nitriles are valuable building blocks in organic synthesis and also have multiple applications in chemical biology and materials science. Due to the high availability of tertiary alcohols, developing methods for their conversion into alkynes an ...

2022

Radical Alkynylations with EthynylBenziodoXolones: From Photocatalysis to Direct Excitation

Jérôme Waser, Stephanie Grace Elizabeth Amos

Ethynylbenziodoxolones (EBXs) have recently emerged as potent reagents for the alkynylation of radicals. Their combination with photocatalysis allows the synthesis of valuable alkynes at room temperature. Herein, we discuss two photomediated strategies for ...

2022

Algebra and geometry of link homology Lecture notes from the IHES 2021 Summer School

Oscar Salomon Kivinen

These notes cover the lectures of the first named author at 2021 IHES Summer School on "Enumerative Geometry, Physics and Representation Theory" with additional details and references. They cover the definition of Khovanov-Rozansky triply graded homology, ...

WILEY2022

High-order accurate entropy stable adaptive moving mesh finite difference schemes for special relativistic (magneto)hydrodynamics

Junming Duan

This paper develops high-order accurate entropy stable (ES) adaptive moving mesh finite difference schemes for the two- and three-dimensional special relativistic hydrodynamic (RHD) and magnetohydrodynamic (RMHD) equations, which is the high-order accurate ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2022