Zobrazeno 1 - 10
of 292
pro vyhledávání: '"P Balaprakash"'
Autor:
Jin, Hongwei, Papadimitriou, George, Raghavan, Krishnan, Zuk, Pawel, Balaprakash, Prasanna, Wang, Cong, Mandal, Anirban, Deelman, Ewa
Anomaly detection in computational workflows is critical for ensuring system reliability and security. However, traditional rule-based methods struggle to detect novel anomalies. This paper leverages large language models (LLMs) for workflow anomaly
Externí odkaz:
http://arxiv.org/abs/2407.17545
Autor:
Pasini, Massimiliano Lupo, Choi, Jong Youl, Mehta, Kshitij, Zhang, Pei, Rogers, David, Bae, Jonghyun, Ibrahim, Khaled Z., Aji, Ashwin M., Schulz, Karl W., Polo, Jorda, Balaprakash, Prasanna
We present our work on developing and training scalable graph foundation models (GFM) using HydraGNN, a multi-headed graph convolutional neural network architecture. HydraGNN expands the boundaries of graph neural network (GNN) in both training scale
Externí odkaz:
http://arxiv.org/abs/2406.12909
Autor:
Jin, Hongwei, Balaprakash, Prasanna, Zou, Allen, Ghysels, Pieter, Krishnapriyan, Aditi S., Mate, Adam, Barnes, Arthur, Bent, Russell
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts. One such approach involves placing transformer neutral blocking device
Externí odkaz:
http://arxiv.org/abs/2405.10389
Autor:
Raghavan, Krishnan, Papadimitriou, George, Jin, Hongwei, Mandal, Anirban, Kiran, Mariam, Balaprakash, Prasanna, Deelman, Ewa
A computational workflow, also known as workflow, consists of tasks that are executed in a certain order to attain a specific computational campaign. Computational workflows are commonly employed in science domains, such as physics, chemistry, genomi
Externí odkaz:
http://arxiv.org/abs/2405.06133
Autor:
Wang, Xiao, Liu, Siyan, Tsaris, Aristeidis, Choi, Jong-Youl, Aji, Ashwin, Fan, Ming, Zhang, Wei, Yin, Junqi, Ashfaq, Moetasim, Lu, Dan, Balaprakash, Prasanna
Earth system predictability is challenged by the complexity of environmental dynamics and the multitude of variables involved. Current AI foundation models, although advanced by leveraging large and heterogeneous data, are often constrained by their
Externí odkaz:
http://arxiv.org/abs/2404.14712
Autor:
Tsaris, Aristeidis, Zhang, Chengming, Wang, Xiao, Yin, Junqi, Liu, Siyan, Ashfaq, Moetasim, Fan, Ming, Choi, Jong Youl, Wahib, Mohamed, Lu, Dan, Balaprakash, Prasanna, Wang, Feiyi
Vision Transformers (ViTs) are pivotal for foundational models in scientific imagery, including Earth science applications, due to their capability to process large sequence lengths. While transformers for text has inspired scaling sequence lengths i
Externí odkaz:
http://arxiv.org/abs/2405.15780
X-ray and electron diffraction-based microscopy use bragg peak detection and ptychography to perform 3-D imaging at an atomic resolution. Typically, these techniques are implemented using computationally complex tasks such as a Psuedo-Voigt function
Externí odkaz:
http://arxiv.org/abs/2404.10689
Autor:
Egele, Romain, Junior, Julio C. S. Jacques, van Rijn, Jan N., Guyon, Isabelle, Baró, Xavier, Clapés, Albert, Balaprakash, Prasanna, Escalera, Sergio, Moeslund, Thomas, Wan, Jun
Machine learning is now used in many applications thanks to its ability to predict, generate, or discover patterns from large quantities of data. However, the process of collecting and transforming data for practical use is intricate. Even in today's
Externí odkaz:
http://arxiv.org/abs/2404.09703
Autor:
Sun, Yixuan, Sowunmi, Ololade, Egele, Romain, Narayanan, Sri Hari Krishna, Van Roekel, Luke, Balaprakash, Prasanna
Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage the advanced search algorithms for multiobjective optimization in DeepHyper, a scalable hyperparameter optimization so
Externí odkaz:
http://arxiv.org/abs/2404.05768
To reach high performance with deep learning, hyperparameter optimization (HPO) is essential. This process is usually time-consuming due to costly evaluations of neural networks. Early discarding techniques limit the resources granted to unpromising
Externí odkaz:
http://arxiv.org/abs/2404.04111