Zobrazeno 1 - 10
of 11 183
pro vyhledávání: '"Seale AN"'
Autor:
Thompson, Andrew, Sommers, Alexander, Russell-Gilbert, Alicia, Cummins, Logan, Mittal, Sudip, Rahimi, Shahram, Seale, Maria, Jaboure, Joseph, Arnold, Thomas, Church, Joshua
Predictive maintenance has been used to optimize system repairs in the industrial, medical, and financial domains. This technique relies on the consistent ability to detect and predict anomalies in critical systems. AI models have been trained to det
Externí odkaz:
http://arxiv.org/abs/2411.05848
Autor:
Russell-Gilbert, Alicia, Sommers, Alexander, Thompson, Andrew, Cummins, Logan, Mittal, Sudip, Rahimi, Shahram, Seale, Maria, Jaboure, Joseph, Arnold, Thomas, Church, Joshua
For data-constrained, complex and dynamic industrial environments, there is a critical need for transferable and multimodal methodologies to enhance anomaly detection and therefore, prevent costs associated with system failures. Typically, traditiona
Externí odkaz:
http://arxiv.org/abs/2411.00914
Autor:
Cummins, Logan, Sommers, Alexander, Mittal, Sudip, Rahimi, Shahram, Seale, Maria, Jaboure, Joseph, Arnold, Thomas
There exists three main areas of study inside of the field of predictive maintenance: anomaly detection, fault diagnosis, and remaining useful life prediction. Notably, anomaly detection alerts the stakeholder that an anomaly is occurring. This raise
Externí odkaz:
http://arxiv.org/abs/2408.11935
Autor:
Lin, Chi-Heng, Gao, Shangqian, Smith, James Seale, Patel, Abhishek, Tuli, Shikhar, Shen, Yilin, Jin, Hongxia, Hsu, Yen-Chang
Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computational requirements make their deployment challenging on devices with limit
Externí odkaz:
http://arxiv.org/abs/2408.09632
Autor:
Hopper, John Seale
This paper will introduce a family of sliced Wasserstein geodesics which are not standard Wasserstein geodesics, objects yet to be discovered in the literature. These objects exhibit how the geometric structure of the Sliced Wasserstein space differs
Externí odkaz:
http://arxiv.org/abs/2407.07219
Autor:
Sommers, Alexander, Cummins, Logan, Mittal, Sudip, Rahimi, Shahram, Seale, Maria, Jaboure, Joseph, Arnold, Thomas
Generative AI has received much attention in the image and language domains, with the transformer neural network continuing to dominate the state of the art. Application of these models to time series generation is less explored, however, and is of g
Externí odkaz:
http://arxiv.org/abs/2406.02322
Autor:
Xu, Wentian, Moffat, Matthew, Seale, Thalia, Liang, Ziyun, Wagner, Felix, Whitehouse, Daniel, Menon, David, Newcombe, Virginia, Voets, Natalie, Banerjee, Abhirup, Kamnitsas, Konstantinos
Publikováno v:
Proceedings of Machine Learning Research, MIDL 2024
Models for segmentation of brain lesions in multi-modal MRI are commonly trained for a specific pathology using a single database with a predefined set of MRI modalities, determined by a protocol for the specific disease. This work explores the follo
Externí odkaz:
http://arxiv.org/abs/2405.18511
An upgraded 0.4-meter telescope fleet for Las Cumbres Observatory's Educational and Science Programs
Autor:
Harbeck, Daniel-Rolf, Taylor, Brook, Kirby, Annie, Bowman, Mark, Foale, Steve, Kadlec, Kal, McCully, Curtis, Daily, Matthew, DeVera, Jon, Douglass, Dave, Willis, Mark, Baker, Ian, Volgenau, Nikolaus, Conway, Patrick, Haworth, Brian, Estrada, Jesus, Gomez, Edward, Seale, Sandy, Hopkinson, Alice, Rios, Fernando, Kotapali, Prerana, Storrie-Lombardi, Lisa, Rosing, Wayne
Las Cumbres Observatory (LCOGT) operates a global network of robotic 0.4, 1.0, and 2.0-meter telescopes to facilitate scientific research and education in time-domain astronomy. LCOGT's flagship educational program, Global Sky Partners (GSP), awards
Externí odkaz:
http://arxiv.org/abs/2405.10408
Autor:
Smith, James Seale, Valkov, Lazar, Halbe, Shaunak, Gutta, Vyshnavi, Feris, Rogerio, Kira, Zsolt, Karlinsky, Leonid
Foundation Models (FMs) have become the hallmark of modern AI, however, these models are trained on massive data, leading to financially expensive training. Updating FMs as new data becomes available is important, however, can lead to `catastrophic f
Externí odkaz:
http://arxiv.org/abs/2404.12526
Autor:
Sommers, Alexander, Ramezani, Somayeh Bakhtiari, Cummins, Logan, Mittal, Sudip, Rahimi, Shahram, Seale, Maria, Jaboure, Joseph
Data augmentation is an important facilitator of deep learning applications in the time series domain. A gap is identified in the literature, demonstrating sparse exploration of the transformer, the dominant sequence model, for data augmentation in t
Externí odkaz:
http://arxiv.org/abs/2404.08601