Zobrazeno 1 - 10
of 986
pro vyhledávání: '"Sousa, Joao P."'
This work introduces JEMA (Joint Embedding with Multimodal Alignment), a novel co-learning framework tailored for laser metal deposition (LMD), a pivotal process in metal additive manufacturing. As Industry 5.0 gains traction in industrial applicatio
Externí odkaz:
http://arxiv.org/abs/2410.23988
Autor:
Froelicher, David, Cho, Hyunghoon, Edupalli, Manaswitha, Sousa, Joao Sa, Bossuat, Jean-Philippe, Pyrgelis, Apostolos, Troncoso-Pastoriza, Juan R., Berger, Bonnie, Hubaux, Jean-Pierre
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
Externí odkaz:
http://arxiv.org/abs/2304.00129
General-purpose operating systems (GPOS), such as Linux, encompass several million lines of code. Statistically, a larger code base inevitably leads to a higher number of potential vulnerabilities and inherently a more vulnerable system. To minimize
Externí odkaz:
http://arxiv.org/abs/2209.05572
A strong geodetic set of a graph~$G=(V,E)$ is a vertex set~$S \subseteq V(G)$ in which it is possible to cover all the remaining vertices of~$V(G) \setminus S$ by assigning a unique shortest path between each vertex pair of~$S$. In the Strong Geodeti
Externí odkaz:
http://arxiv.org/abs/2208.01796
Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. Previous work has focused on providing concepts for specific models (eg, neural networks) or data types (eg, images), and by either trying to
Externí odkaz:
http://arxiv.org/abs/2205.03601
With upcoming blockchain infrastructures, world-spanning Byzantine consensus is getting practical and necessary. In geographically distributed systems, the pace at which consensus is achieved is limited by the heterogenous latencies of connections be
Externí odkaz:
http://arxiv.org/abs/2011.01671
Autor:
Sav, Sinem, Pyrgelis, Apostolos, Troncoso-Pastoriza, Juan R., Froelicher, David, Bossuat, Jean-Philippe, Sousa, Joao Sa, Hubaux, Jean-Pierre
In this paper, we address the problem of privacy-preserving training and evaluation of neural networks in an $N$-party, federated learning setting. We propose a novel system, POSEIDON, the first of its kind in the regime of privacy-preserving neural
Externí odkaz:
http://arxiv.org/abs/2009.00349
This paper addresses the problem of steering a robotic vehicle along a geometric path specified with respect to a reference frame moving in three dimensions, termed the Moving Path Following (MPF) motion control problem. The MPF motion control proble
Externí odkaz:
http://arxiv.org/abs/2007.02044
Autor:
Froelicher, David, Troncoso-Pastoriza, Juan R., Pyrgelis, Apostolos, Sav, Sinem, Sousa, Joao Sa, Bossuat, Jean-Philippe, Hubaux, Jean-Pierre
In this paper, we address the problem of privacy-preserving distributed learning and the evaluation of machine-learning models by analyzing it in the widespread MapReduce abstraction that we extend with privacy constraints. We design SPINDLE (Scalabl
Externí odkaz:
http://arxiv.org/abs/2005.09532
The popularization of blockchains leads to a resurgence of interest in Byzantine Fault-Tolerant (BFT) state machine replication protocols. However, much of the work on this topic focuses on the underlying consensus protocols, with emphasis on their l
Externí odkaz:
http://arxiv.org/abs/2004.14527