Leveraging Social Networks for Increased BitTorrent Robustness

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Geometry of the Loss Landscape in Overparameterized Neural Networks: Symmetries and Invariances

Wulfram Gerstner, Clément Hongler, Johanni Michael Brea, Francesco Spadaro, Berfin Simsek, Arthur Jacot

We study how permutation symmetries in overparameterized multi-layer neural networks generate `symmetry-induced' critical points. Assuming a network with

L

layers of minimal widths

r_1^*, \ldots, r_{L-1}^*

reaches a zero-loss minimum at $ r_1^*! \c ...

2021

Spikyball Sampling: Exploring Large Networks via an Inhomogeneous Filtered Diffusion

Nicolas Aspert, Volodymyr Miz, Benjamin Ricaud

Studying real-world networks such as social networks or web networks is a challenge. These networks often combine a complex, highly connected structure together with a large size. We propose a new approach for large scale networks that is able to automatic ...

2020

Operating System and Network Co-Design for Latency-Critical Datacenter Applications

Evangelos Marios Kogias

Datacenters are the heart of our digital lives. Online applications, such as social-networking and e-commerce, run inside datacenters under strict Service Level Objectives for their tail latency. Tight latency SLOs are necessary for such services to remain ...

EPFL2020

Dynamic pattern recognition in large-scale graphs with applications to social networks

Volodymyr Miz

A graph is a versatile data structure facilitating representation of interactions among objects in various complex systems. Very often these objects have attributes whose measurements change over time, reflecting the dynamics of the system. This general da ...

EPFL2020

DELF: Safeguarding deletion correctness in Online Social Networks

Georgios Damaskinos

Deletion is a core facet of Online Social Networks (OSNs). For users, deletion is a tool to remove what they have shared and control their data. For OSNs, robust deletion is both an obligation to their users and a risk when developer mistakes inevitably oc ...

USENIX ASSOC2020

Robust P2P Personalized Learning

Rachid Guerraoui, Yahya Benkaouz

Decentralized machine learning over peer-to-peer networks is very appealing for it enables to learn personalized models without sharing users data, nor relying on any central server. Peers can improve upon their locally trained model across a network graph ...

IEEE2020

Scale-dependent measure of network centrality from diffusion dynamics

Alexis Arnaudon

Classic measures of graph centrality capture distinct aspects of node importance, from the local (e.g., degree) to the global (e.g., closeness). Here we exploit the connection between diffusion and geometry to introduce a multiscale centrality measure. A n ...

AMER PHYSICAL SOC2020

Global robustness versus local vulnerabilities in complex synchronous networks

Philippe René Joseph Jacquod

In complex network-coupled dynamical systems, two questions of central importance are how to identify the most vulnerable components and how to devise a network making the overall system more robust to external perturbations. To address these two questions ...

2019

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