Zobrazeno 1 - 10
of 125
pro vyhledávání: '"Kim, Kibaek"'
Autor:
Li, Yijiang, Kim, Kibaek, Leyffer, Sven, Menickelly, Matt, Lewis, Lawrence Paul, Bergerson, Joshua
Physical infrastructure systems supply crucial resources to residential, commercial, and industrial activities. These infrastructure systems generally consist of multiple types of infrastructure assets that are interdependent. In the event of a disas
Externí odkaz:
http://arxiv.org/abs/2407.16796
Autor:
Hamann, Hendrik F., Brunschwiler, Thomas, Gjorgiev, Blazhe, Martins, Leonardo S. A., Puech, Alban, Varbella, Anna, Weiss, Jonas, Bernabe-Moreno, Juan, Massé, Alexandre Blondin, Choi, Seong, Foster, Ian, Hodge, Bri-Mathias, Jain, Rishabh, Kim, Kibaek, Mai, Vincent, Mirallès, François, De Montigny, Martin, Ramos-Leaños, Octavio, Suprême, Hussein, Xie, Le, Youssef, El-Nasser S., Zinflou, Arnaud, Belvi, Alexander J., Bessa, Ricardo J., Bhattari, Bishnu Prasad, Schmude, Johannes, Sobolevsky, Stanislav
Foundation models (FMs) currently dominate news headlines. They employ advanced deep learning architectures to extract structural information autonomously from vast datasets through self-supervision. The resulting rich representations of complex syst
Externí odkaz:
http://arxiv.org/abs/2407.09434
Autor:
Iakovidou, Charikleia, Kim, Kibaek
Federated learning (FL) was recently proposed to securely train models with data held over multiple locations ("clients") under the coordination of a central server. Two major challenges hindering the performance of FL algorithms are long training ti
Externí odkaz:
http://arxiv.org/abs/2405.10123
The advent of smart meters has enabled pervasive collection of energy consumption data for training short-term load forecasting models. In response to privacy concerns, federated learning (FL) has been proposed as a privacy-preserving approach for tr
Externí odkaz:
http://arxiv.org/abs/2404.01517
The transformative impact of large language models (LLMs) like LLaMA and GPT on natural language processing is countered by their prohibitive computational demands. Pruning has emerged as a pivotal compression strategy, introducing sparsity to enhanc
Externí odkaz:
http://arxiv.org/abs/2402.17946
Autor:
Li, Zilinghan, He, Shilan, Chaturvedi, Pranshu, Kindratenko, Volodymyr, Huerta, Eliu A, Kim, Kibaek, Madduri, Ravi
Federated learning enables multiple data owners to collaboratively train robust machine learning models without transferring large or sensitive local datasets by only sharing the parameters of the locally trained models. In this paper, we elaborate o
Externí odkaz:
http://arxiv.org/abs/2402.12271
Autor:
Wilkins, Grant, Di, Sheng, Calhoun, Jon C., Li, Zilinghan, Kim, Kibaek, Underwood, Robert, Mortier, Richard, Cappello, Franck
With the promise of federated learning (FL) to allow for geographically-distributed and highly personalized services, the efficient exchange of model updates between clients and servers becomes crucial. FL, though decentralized, often faces communica
Externí odkaz:
http://arxiv.org/abs/2312.13461
Autor:
Hoang, Trung-Hieu, Fuhrman, Jordan, Madduri, Ravi, Li, Miao, Chaturvedi, Pranshu, Li, Zilinghan, Kim, Kibaek, Ryu, Minseok, Chard, Ryan, Huerta, E. A., Giger, Maryellen
Facilitating large-scale, cross-institutional collaboration in biomedical machine learning projects requires a trustworthy and resilient federated learning (FL) environment to ensure that sensitive information such as protected health information is
Externí odkaz:
http://arxiv.org/abs/2312.08701
The widespread adoption of smart meters provides access to detailed and localized load consumption data, suitable for training building-level load forecasting models. To mitigate privacy concerns stemming from model-induced data leakage, federated le
Externí odkaz:
http://arxiv.org/abs/2312.00036
We consider the unit commitment (UC) problem that employs the alternating current optimal power flow (ACOPF) constraints, which is formulated as a mixed-integer nonlinear programming problem and thus challenging to solve in practice. We develop a new
Externí odkaz:
http://arxiv.org/abs/2310.13145