Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Ranadive, Teresa"'
Autor:
Ding, Mucong, Xu, Yuancheng, Rabbani, Tahseen, Liu, Xiaoyu, Gravelle, Brian, Ranadive, Teresa, Tuan, Tai-Ching, Huang, Furong
Dataset condensation can be used to reduce the computational cost of training multiple models on a large dataset by condensing the training dataset into a small synthetic set. State-of-the-art approaches rely on matching the model gradients between t
Externí odkaz:
http://arxiv.org/abs/2405.17535
Autor:
Laukemann, Jan, Helal, Ahmed E., Anderson, S. Isaac Geronimo, Checconi, Fabio, Soh, Yongseok, Tithi, Jesmin Jahan, Ranadive, Teresa, Gravelle, Brian J, Petrini, Fabrizio, Choi, Jee
High-dimensional sparse data emerge in many critical application domains such as cybersecurity, healthcare, anomaly detection, and trend analysis. To quickly extract meaningful insights from massive volumes of these multi-dimensional data, scientists
Externí odkaz:
http://arxiv.org/abs/2403.06348
Ising machines are a form of quantum-inspired processing-in-memory computer which has shown great promise for overcoming the limitations of traditional computing paradigms while operating at a fraction of the energy use. The process of designing Isin
Externí odkaz:
http://arxiv.org/abs/2310.16246
Autor:
Nguyen, Andy, Helal, Ahmed E., Checconi, Fabio, Laukemann, Jan, Tithi, Jesmin Jahan, Soh, Yongseok, Ranadive, Teresa, Petrini, Fabrizio, Choi, Jee W.
Tensor decomposition (TD) is an important method for extracting latent information from high-dimensional (multi-modal) sparse data. This study presents a novel framework for accelerating fundamental TD operations on massively parallel GPU architectur
Externí odkaz:
http://arxiv.org/abs/2201.12523
Autor:
Helal, Ahmed E., Laukemann, Jan, Checconi, Fabio, Tithi, Jesmin Jahan, Ranadive, Teresa, Petrini, Fabrizio, Choi, Jeewhan
The analysis of high-dimensional sparse data is becoming increasingly popular in many important domains. However, real-world sparse tensors are challenging to process due to their irregular shapes and data distributions. We propose the Adaptive Linea
Externí odkaz:
http://arxiv.org/abs/2102.10245
Autor:
Su J; Department of Electrical and Computer Engineering, University of Maryland, College Park, MD, United States., Li J; Department of Computer Science, University of Maryland, College Park, MD, United States., Liu X; Department of Computer Science, University of Maryland, College Park, MD, United States., Ranadive T; Laboratory for Physical Sciences, University of Maryland, College Park, MD, United States., Coley C; Department of Aeronautics, United States Air Force Academy, Colorado Springs, CO, United States., Tuan TC; Laboratory for Physical Sciences, University of Maryland, College Park, MD, United States., Huang F; Department of Computer Science, University of Maryland, College Park, MD, United States.
Publikováno v:
Frontiers in artificial intelligence [Front Artif Intell] 2022 Mar 08; Vol. 5, pp. 728761. Date of Electronic Publication: 2022 Mar 08 (Print Publication: 2022).