Publication
Linearization and Identification of Multiple-Attractor Dynamical Systems through Laplacian Eigenmaps
Publications associées (36)
Reactive collision-free motion generation in joint space via dynamical systems and sampling-based MPC
Aude Billard, Mikhail Koptev, Nadia Barbara Figueroa Fernandez
Dynamical system (DS) based motion planning offers collision-free motion, with closed-loop reactivity thanks to their analytical expression. It ensures that obstacles are not penetrated by reshaping a nominal DS through matrix modulation, which is construc ...
Sage Publications Ltd2024Unsupervised Graph Representation Learning with Cluster-aware Self-training and Refining
Unsupervised graph representation learning aims to learn low-dimensional node embeddings without supervision while preserving graph topological structures and node attributive features. Previous Graph Neural Networks (GNN) require a large number of labeled ...
New York2023The dynamics of unsteady frictional slip pulses
Thibault Didier Roch, Fabian Barras
Self-healing slip pulses are major spatiotemporal failure modes of frictional systems, featuring a characteristic size L(t) and a propagation velocity c(p)(t) (t is time). Here, we develop a theory of slip pulses in realistic rate- and state-dependent fric ...
Washington2023Improving K-means Clustering Using Speculation
Anastasia Ailamaki, Viktor Sanca, Eleni Zapridou, Stefan Igescu
K-means is one of the fundamental unsupervised data clustering and machine learning methods. It has been well studied over the years: parallelized, approximated, and optimized for different cases and applications. With increasingly higher parallelism leadi ...
2023Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap
Florent Gérard Krzakala, Lenka Zdeborová, Luca Pesce, Bruno Loureiro
A simple model to study subspace clustering is the high-dimensional k -Gaussian mixture model where the cluster means are sparse vectors. Here we provide an exact asymptotic characterization of the statistically optimal reconstruction error in this model i ...
2022Manifold 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 ...
2022Discriminative clustering with representation learning with any ratio of labeled to unlabeled data
We present a discriminative clustering approach in which the feature representation can be learned from data and moreover leverage labeled data. Representation learning can give a similarity-based clustering method the ability to automatically adapt to an ...
2022Sublinear Algorithms for Spectral Graph Clustering
This thesis focuses on designing spectral tools for graph clustering in sublinear time. With the emergence of big data, many traditional polynomial time, and even linear time algorithms have become prohibitively expensive. Processing modern datasets requir ...
EPFL2021On Perfect Clustering of High Dimension, Low Sample Size Data
Popular clustering algorithms based on usual distance functions (e.g., the Euclidean distance) often suffer in high dimension, low sample size (HDLSS) situations, where concentration of pairwise distances and violation of neighborhood structure have advers ...
IEEE COMPUTER SOC2020Chemiscope: interactive structure-property explorer for materials and molecules
Michele Ceriotti, Guillaume André Jean Fraux
The number of materials or molecules that can be created by combining different chemical elements in various proportions and spatial arrangements is enormous. Computational chemistry can be used to generate databases containing billions of potential struct ...
2020