Publications related to Subspaces Indexing Model on Grassmann Manifold for Image Search

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Scalable and Privacy-Preserving Federated Principal Component Analysis

Jean-Pierre Hubaux, Juan Ramón Troncoso-Pastoriza, Jean-Philippe Léonard Bossuat, Apostolos Pyrgelis, David Jules Froelicher, Joao André Gomes de Sá e Sousa

Principal component analysis (PCA) is an essential algorithm for dimensionality reduction in many data science domains. We address the problem of performing a federated PCA on private data distributed among multiple data providers while ensuring data confi ...

IEEE COMPUTER SOC2023

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Lijing Xin, Daniel Wenz, Mark Stephan Widmaier, Songi Lim

A new and efficient magnetisation transfer P-31 magnetic resonance fingerprinting (MT-P-31-MRF) approach is introduced to measure the creatine kinase metabolic rate kCK

{k}_{\mathrm{CK}}

between phosphocreatine (PCr) and adenosine triphosphate (ATP) i ...

WILEY2023

3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping

Zhuoqian Yang, Siyuan Li

We present 3DHumanGAN, a 3D-aware generative adversarial network that synthesizes photo-like images of fullbody humans with consistent appearances under different view-angles and body-poses. To tackle the representational and computational challenges in sy ...

Ieee Computer Soc2023

Leveraging topology, geometry, and symmetries for efficient Machine Learning

Michaël Defferrard

When learning from data, leveraging the symmetries of the domain the data lies on is a principled way to combat the curse of dimensionality: it constrains the set of functions to learn from. It is more data efficient than augmentation and gives a generaliz ...

EPFL2022

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Aude Billard, Bernardo Fichera

Dynamical Systems (DS) are fundamental to the modeling and understanding time evolving phenomena, and have application in physics, biology and control. As determining an analytical description of the dynamics is often difficult, data-driven approaches are ...

MICROTOME PUBL2022

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

In this work we introduce a manifold learning-based method for uncertainty quantification (UQ) in systems describing complex spatiotemporal processes. Our first objective is to identify the embedding of a set of high-dimensional data representing quantitie ...

2022

Linear and nonlinear substructured Restricted Additive Schwarz iterations and preconditioning

Tommaso Vanzan

Iterative substructuring Domain Decomposition (DD) methods have been extensively studied, and they are usually associated with nonoverlapping decompositions. It is less known that classical overlapping DD methods can also be formulated in substructured for ...

2022

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Hervé Bourlard, Selen Hande Kabil

Starting from a strong Lattice-Free Maximum Mutual Information (LF-MMI) baseline system, we explore different autoencoder configurations to enhance Mel-Frequency Cepstral Coefficients (MFCC) features. Autoencoders are expected to generate new MFCC features ...

ISCA-INT SPEECH COMMUNICATION ASSOC2022

Poly-NL: Linear Complexity Non-local Layers With 3rd Order Polynomials

Grigorios Chrysos, Filippos Kokkinos

Spatial self-attention layers, in the form of Non-Local blocks, introduce long-range dependencies in Convolutional Neural Networks by computing pairwise similarities among all possible positions. Such pairwise functions underpin the effectiveness of non-lo ...

IEEE2021

Subspaces Indexing Model on Grassmann Manifold for Image Search

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Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Scalable and Privacy-Preserving Federated Principal Component Analysis

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping

Leveraging topology, geometry, and symmetries for efficient Machine Learning

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

Linear and nonlinear substructured Restricted Additive Schwarz iterations and preconditioning

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition

Poly-NL: Linear Complexity Non-local Layers With 3rd Order Polynomials

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

3DHumanGAN: 3D-Aware Human Image Generation with 3D Pose Mapping

Scalable and Privacy-Preserving Federated Principal Component Analysis

Fast in vivo assay of creatine kinase activity in the human brain by P-31 magnetic resonance fingerprinting

Leveraging topology, geometry, and symmetries for efficient Machine Learning

Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps

Linear and nonlinear substructured Restricted Additive Schwarz iterations and preconditioning

Poly-NL: Linear Complexity Non-local Layers With 3rd Order Polynomials

Manifold Learning-Based Polynomial Chaos Expansions For High-Dimensional Surrogate Models

From Undercomplete to Sparse Overcomplete Autoencoders to Improve LF-MMI Speech Recognition