Publications related to Smaller, Faster & Lighter KNN Graph Constructions

Graph generative deep learning models with an application to circuit topologies

Modern integrated circuits are tiny yet incredibly complex technological artifacts, composed of millions and billions of individual structures working in unison. Managing their complexity and facilitating their design drove part of the co-evolution of mode ...

EPFL2024

Equivariant Neural Architectures for Representing and Generating Graphs

Clément Arthur Yvon Vignac

Graph machine learning offers a powerful framework with natural applications in scientific fields such as chemistry, biology and material sciences. By representing data as a graph, we encode the prior knowledge that the data is composed of a set of entiti ...

EPFL2023

A full characterization of invariant embeddability of unimodular planar graphs

Laszlo Marton Toth

When can a unimodular random planar graph be drawn in the Euclidean or the hyperbolic plane in a way that the distribution of the random drawing is isometry-invariant? This question was answered for one-ended unimodular graphs in Benjamini and Timar, using ...

WILEY2023

Robust Collaborative Learning with Linear Gradient Overhead

Rachid Guerraoui, Le Nguyen Hoang, John Stephan, Sadegh Farhadkhani, Nirupam Gupta, Rafaël Benjamin Pinot

Collaborative learning algorithms, such as distributed SGD (or D-SGD), are prone to faulty machines that may deviate from their prescribed algorithm because of software or hardware bugs, poisoned data or malicious behaviors. While many solutions have been ...

PLMR2023

Transfer learning application of self-supervised learning in ARPES

Gabriel Aeppli

There is a growing recognition that electronic band structure is a local property of materials and devices, and there is steep growth in capabilities to collect the relevant data. New photon sources, from small-laboratory-based lasers to free electron lase ...

IOP Publishing Ltd2023

Dataset for "Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid"

John Martin Kolinski

This dataset supports the publication 'Elastocapillary menisci mediate interaction of neighboring structures at the surface of a compliant solid' by Lebo Molefe and John M. Kolinski, Physical Review E, (2023). The data are surface profiles of textured surf ...

Zenodo2023

Data for "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis"

Cristina Martin Olmos, Anders Meibom, Stéphane Laurent Escrig, Gaëlle Delphine Toullec, Nils Rädecker, Claudia Isabella Pogoreutz, Guilhem Maurice Louis Banc-Prandi

Raw data associated with the publication "Host starvation and in hospite degradation of algal symbionts shape the heat stress response of the Cassiopea-Symbiodiniaceae symbiosis". Temperature profile, daily measurements, physiological measurements, element ...

Zenodo2023

Lithium tantalate photonic integrated circuits for volume manufacturing

Tobias Kippenberg

Dataset for the manuscript "Lithium tantalate photonic integrated circuits for volume manufacturing". DOI: 10.1038/s41586-024-07369-1 Contains all raw data and code used to produce the Figures and Extended Data Figures in the manuscript. ...

EPFL Infoscience2023

Imaging radial distribution functions of complex particles by relayed dynamic nuclear polarization

Arthur César Pinon, David Lyndon Emsley, Pierrick Berruyer

Raw data and Matlab scripts from the publication at https://doi.org/10.1021/jacs.3c01279 All codes are provided free of charge and come with no warranty. Future use (original or modified) is required to cite: Zenedo data deposit at DOI 10.5281/zenodo. ...

Zenodo2023

Data for "Ultrafast deep-ultraviolet study of photoexcited aqueous Ferrioxalate"

Majed Chergui, Malte Oppermann, Michele Puppin, Christoph Bostedt, Hui-Yuan Chen

Data repository for the following publication: A. Duque-Prata*, H.-Y. Chen*, L. Müller, M. Oppermann, M. Puppin, C. Serpa, C. Bostedt and M. Chergui, “Ultrafast deep-ultraviolet study of photoexcited aqueous Ferrioxalate”, submitted to ChemPhotoCh ...

Zenodo2023

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems"

Michaël Unser, Stanislas Ducotterd, Pakshal Narendra Bohra, Alexis Marie Frederic Goujon, Sebastian Jonas Neumayer

Data for: "A Neural-Network-Based Convex Regularizer for Inverse Problems". The corresponding scripts can be accessed on GitHub (https://github.com/axgoujon/convex_ridge_regularizers). The data is organized as follows: - ct_data_sets.tar.gz: contains prepr ...

Zenodo2023

Physically Consistent Multiple-Step Data-Driven Predictions Using Physics-Based Filters

Colin Neil Jones, Yingzhao Lian, Jicheng Shi

Data-driven control can facilitate the rapid development of controllers, offering an alternative to conventional approaches. In order to maintain consistency between any known underlying physical laws and a data-driven decision-making process, preprocessin ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Reduced representations of complexes, signals, and multifiltrations

Celia Camille Hacker

The field of computational topology has developed many powerful tools to describe the shape of data, offering an alternative point of view from classical statistics. This results in a variety of complex structures that are not always directly amenable for ...

EPFL2022

Sketches, metrics and fast algorithms

Navid Nouri

As it has become easier and cheaper to collect big datasets in the last few decades, designing efficient and low-cost algorithms for these datasets has attracted unprecedented attention. However, in most applications, even storing datasets as acquired has ...

EPFL2022

A Topological characterisation of Weisfeiler-Leman equivalence classes

Graph Neural Networks (GNNs) are learning models aimed at processing graphs and signals on graphs. The most popular and successful GNNs are based on message passing schemes. Such schemes inherently have limited expressive power when it comes to distinguish ...

2022

Graph Representation Learning with Optimal Transport: Analysis and Applications

Effrosyni Simou

In several machine learning settings, the data of interest are well described by graphs. Examples include data pertaining to transportation networks or social networks. Further, biological data, such as proteins or molecules, lend themselves well to graph- ...

EPFL2022

Graph Embedding for Retrieval

Chi Thang Duong

Information retrieval (IR) systems such as search engines are important for people to find what they need among the tremendous amount of data available in their organization or on the Internet. These IR systems enable users to search in a large data collec ...

EPFL2022

Sequential metric dimension for random graphs

Patrick Thiran, Gergely Odor

In the localization game on a graph, the goal is to find a fixed but unknown target node v* with the least number of distance queries possible. In the j-th step of the game, the player queries a single node v_j and receives, as an answer to their query, th ...

2021

Scalable Robust Graph Embedding with Spark

Karl Aberer, Quoc Viet Hung Nguyen, Chi Thang Duong, Trung-Dung Hoang

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

ASSOC COMPUTING MACHINERY2021

Variants in USP48 encoding ubiquitin hydrolase are associated with autosomal dominant non-syndromic hereditary hearing loss

Nicolas Jean Philippe Guex

Non-Syndromic Hereditary Hearing Loss (NSHHL) is a genetically heterogeneous sensory disorder with about 120 genes already associated. Through exome sequencing (ES) and data aggregation, we identified a family with six affected individuals and one unrelate ...

Oxford University Press2021