Zobrazeno 1 - 10
of 40 467
pro vyhledávání: '"A. de Vos"'
Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level o
Externí odkaz:
http://arxiv.org/abs/2410.02541
Rolling origin forecast instability refers to variability in forecasts for a specific period induced by updating the forecast when new data points become available. Recently, an extension to the N-BEATS model for univariate time series point forecast
Externí odkaz:
http://arxiv.org/abs/2409.18267
Autor:
Albert, Julien, Balfroid, Martin, Doh, Miriam, Bogaert, Jeremie, La Fisca, Luca, De Vos, Liesbet, Renard, Bryan, Stragier, Vincent, Jean, Emmanuel
Recommender systems have become integral to our digital experiences, from online shopping to streaming platforms. Still, the rationale behind their suggestions often remains opaque to users. While some systems employ a graph-based approach, offering
Externí odkaz:
http://arxiv.org/abs/2409.06297
Autor:
Autthasan, Phairot, Chaisaen, Rattanaphon, Phan, Huy, De Vos, Maarten, Wilaiprasitporn, Theerawit
Publikováno v:
IEEE Internet of Things Journal 2024
Recent advances in deep learning (DL) have significantly impacted motor imagery (MI)-based brain-computer interface (BCI) systems, enhancing the decoding of electroencephalography (EEG) signals. However, most studies struggle to identify discriminati
Externí odkaz:
http://arxiv.org/abs/2409.04104
Federated learning is a new approach to distributed machine learning that offers potential advantages such as reducing communication requirements and distributing the costs of training algorithms. Therefore, it could hold great promise in swarm robot
Externí odkaz:
http://arxiv.org/abs/2409.01900
Autor:
Guerraoui, Rachid, Kermarrec, Anne-Marie, Kucherenko, Anastasiia, Pinot, Rafael, de Vos, Martijn
The ability of a peer-to-peer (P2P) system to effectively host decentralized applications often relies on the availability of a peer-sampling service, which provides each participant with a random sample of other peers. Despite the practical effectiv
Externí odkaz:
http://arxiv.org/abs/2408.03829
Autor:
Dhasade, Akash, Dini, Paolo, Guerra, Elia, Kermarrec, Anne-Marie, Miozzo, Marco, Pires, Rafael, Sharma, Rishi, de Vos, Martijn
Decentralized learning (DL) offers a powerful framework where nodes collaboratively train models without sharing raw data and without the coordination of a central server. In the iterative rounds of DL, models are trained locally, shared with neighbo
Externí odkaz:
http://arxiv.org/abs/2407.01283
In order to examine where, how and why the quenching of star formation begins in the outskirts of galaxy clusters, we investigate the de-projected radial distribution of a large sample of quenched and star-forming galaxies (SFGs) out to $30R_{500}$ a
Externí odkaz:
http://arxiv.org/abs/2406.02196
Federated Learning (FL) is a machine learning approach where nodes collaboratively train a global model. As more nodes participate in a round of FL, the effectiveness of individual model updates by nodes also diminishes. In this study, we increase th
Externí odkaz:
http://arxiv.org/abs/2405.15644
A tree-packing is a collection of spanning trees of a graph. It has been a useful tool for computing the minimum cut in static, dynamic, and distributed settings. In particular, [Thorup, Comb. 2007] used them to obtain his dynamic min-cut algorithm w
Externí odkaz:
http://arxiv.org/abs/2405.09141