Zobrazeno 1 - 10
of 131
pro vyhledávání: '"Kim, Kibaek"'
Accurate short-term energy consumption forecasting for commercial buildings is crucial for smart grid operations. While smart meters and deep learning models enable forecasting using past data from multiple buildings, data heterogeneity from diverse
Externí odkaz:
http://arxiv.org/abs/2411.14421
Large Language Models (LLMs) achieve state-of-the-art performance but are challenging to deploy due to their high computational and storage demands. Pruning can reduce model size, yet existing methods assume public access to calibration data, which i
Externí odkaz:
http://arxiv.org/abs/2410.14852
The concept of a learning healthcare system (LHS) envisions a self-improving network where multimodal data from patient care are continuously analyzed to enhance future healthcare outcomes. However, realizing this vision faces significant challenges
Externí odkaz:
http://arxiv.org/abs/2409.19756
Federated learning (FL) is a distributed machine learning paradigm enabling collaborative model training while preserving data privacy. In today's landscape, where most data is proprietary, confidential, and distributed, FL has become a promising app
Externí odkaz:
http://arxiv.org/abs/2409.11585
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, Belyi, Alexander J., Bessa, Ricardo J., Bhattarai, 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