Publications associées à Réseau en anneau

Estimating the Topology of Preferential Attachment Graphs Under Partial Observability

This work addresses the problem of learning the topology of a network from the signals emitted by the network nodes. These signals are generated over time through a linear diffusion process, where neighboring nodes exchange messages according to the underl ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2023

Investigating cognitive ability using action-based models of structural brain networks

Enrico Amico

Recent developments in network neuroscience have highlighted the importance of developing techniques for analysing and modelling brain networks. A particularly powerful approach for studying complex neural systems is to formulate generative models that use ...

OXFORD UNIV PRESS2022

Frugal Decentralized Learning

Anne-Marie Kermarrec

Machine learning is currently shifting from a centralized paradigm to decentralized ones where machine learning models are trained collaboratively. In fully decentralized learning algorithms, data remains where it was produced, models are trained locally a ...

IEEE2022

Learning Bollobas-Riordan Graphs Under Partial Observability

Ali H. Sayed, Michele Cirillo

This work examines the problem of learning the topology of a network (graph learning) from the signals produced at a subset of the network nodes (partial observability). This challenging problem was recently tackled assuming that the topology is drawn acco ...

IEEE2021

Consensus Control for Decentralized Deep Learning

Martin Jaggi, Tao Lin, Sebastian Urban Stich, Anastasiia Koloskova, Lingjing Kong

Decentralized training of deep learning models enables on-device learning over networks, as well as efficient scaling to large compute clusters. Experiments in earlier works reveal that, even in a data-center setup, decentralized training often suffers fro ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

Quasi-Global Momentum: Accelerating Decentralized Deep Learning on Heterogeneous Data

Martin Jaggi, Tao Lin, Sebastian Urban Stich, Sai Praneeth Reddy Karimireddy

Decentralized training of deep learning models is a key element for enabling data privacy and on-device learning over networks. In realistic learning scenarios, the presence of heterogeneity across different clients' local datasets poses an optimization ch ...

JMLR-JOURNAL MACHINE LEARNING RESEARCH2021

On Existence of Equilibria, Voltage Balancing, and Current Sharing in Consensus-Based DC Microgrids

Giancarlo Ferrari Trecate, Pulkit Nahata

This work presents new secondary regulators for current sharing and voltage balancing in DC microgrids, composed of distributed generation units, dynamic RLC lines, and nonlinear ZIP (constant impedance, constant current, and constant power) loads. The pro ...

IEEE2020

Optimized Quantization in Distributed Graph Signal Processing

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing methods require that the graph nodes communicate by exchanging messages. These messages have a finite precision in a realistic network, which may necessitate to implement quantization. Quantization, in turn, generates er ...

IEEE2019

Consistent Tomography Under Partial Observations Over Adaptive Networks

Ali H. Sayed

This paper studies the problem of inferring whether an agent is directly influenced by another agent over a network. Agent i influences agent j if they are connected (according to the network topology), and if agent j uses the data from agent i to update i ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2019

Reliable and Robust Cyber-Physical Systems for Real-Time Control of Electric Grids

Wajeb Saab

Real-time control of electric grids is a novel approach to handling the increasing penetration of distributed and volatile energy generation brought about by renewables. Such control occurs in cyber-physical systems (CPSs), in which software agents maintai ...

EPFL2019

Optimized Quantization in Distributed Graph Signal Filtering

Pascal Frossard, Isabela Cunha Maia Nobre

Distributed graph signal processing algorithms require the network nodes to communicate by exchanging messages in order to achieve a common objective. These messages have a finite precision in realistic networks, which may necessitate to implement message ...

2019

Voltage Control in Active Distribution Networks Under Uncertainty in the System Model: A Robust Optimization Approach

Mario Paolone, Konstantina Christakou, Ali Abur

Within the context of ancillary services for Active Distribution Networks (ADNs), application of intelligent control techniques is required in order to achieve specific operation objectives. Despite their differences, most control mechanisms proposed in th ...

2018

Indole Alkaloids, Macrocycles and Heterocycles : Interplay between Methodological Development and Total Synthesis

Cyril Piemontesi

An enantioselective total synthesis of (-)-terengganensine A, a heptacyclic monoterpene indole alkaloid, was performed. A short sequence allowed to obtain the enantio-enriched target in good overall yield. The synthesis featured a key asymmetric transfer h ...

EPFL2018

On the Structure of Cortical Microcircuits Inferred from Small Sample Sizes

Rodrigo de Campos Perin

The structure in cortical microcircuits deviates from what would be expected in a purely random network, which has been seen as evidence of clustering. To address this issue, we sought to reproduce the nonrandom features of cortical circuits by considering ...

Soc Neuroscience2017

Physical Layer Cooperation : Theory and Practice

Ayan Sengupta

Information theory has long pointed to the promise of physical layer cooperation in boosting the spectral efficiency of wireless networks. Yet, the optimum relaying strategy to achieve the network capacity has till date remained elusive. Recently however, ...

EPFL2015

Cooperative Relaying at Finite SNR-Role of Quantize-Map-and-Forward

Christina Fragouli, Ayan Sengupta, I-Hsiang Wang

This paper contributes to the design and analysis of Quantize-Map-and-Forward (QMF) relaying by optimizing its performance for small relay networks. QMF was proved to achieve the capacity of arbitrary networks within a bounded gap, as well as the optimal d ...

Ieee-Inst Electrical Electronics Engineers Inc2014

Decoding Delay Minimization in Inter-Session Network Coding

Pascal Frossard, Nikolaos Thomos, Eirina Bourtsoulatze

Intra-session network coding has been shown to offer significant gains in terms of achievable throughput and delay in settings where one source multicasts data to several clients. In this paper, we consider a more general scenario where multiple sources tr ...

Institute of Electrical and Electronics Engineers2014

Robust Localization from Incomplete Local Information

Amin Karbasi

We consider the problem of localizing wireless devices in an ad-hoc network embedded in a

d

-dimensional Euclidean space. Obtaining a good estimation of where wireless devices are located is crucial in wireless network applications including environment m ...

Ieee-Inst Electrical Electronics Engineers Inc2013

Inference of neuronal network spike dynamics and topology from calcium imaging data

Wulfram Gerstner, Friedemann Zenke, Joao Emanuel Felipe Gerhard

Two-photon calcium imaging enables functional analysis of neuronal circuits by inferring action potential (AP) occurrence ("spike trains") from cellular fluorescence signals. It remains unclear how experimental parameters such as signal-to-noise ratio (SNR ...

Frontiers Research Foundation2013

A High-Throughput Byzantine Fault-Tolerant Protocol

Nikola Knezevic

State-machine replication (SMR) is a software technique for tolerating failures and for providing high availability in large-scale systems, through the use of commodity hardware. A replicated state-machine comprises a number of replicas, each of which runs ...

EPFL2012