Zobrazeno 1 - 10
of 617
pro vyhledávání: '"Faloutsos, P."'
Autor:
Masserano, Luca, Ansari, Abdul Fatir, Han, Boran, Zhang, Xiyuan, Faloutsos, Christos, Mahoney, Michael W., Wilson, Andrew Gordon, Park, Youngsuk, Rangapuram, Syama, Maddix, Danielle C., Wang, Yuyang
How to best develop foundational models for time series forecasting remains an important open question. Tokenization is a crucial consideration in this effort: what is an effective discrete vocabulary for a real-valued sequential input? To address th
Externí odkaz:
http://arxiv.org/abs/2412.05244
How can we utilize state-of-the-art NLP tools to better understand legislative deliberation? Committee hearings are a core feature of any legislature, and they offer an institutional setting which promotes the exchange of arguments and reasoning that
Externí odkaz:
http://arxiv.org/abs/2407.06149
Autor:
Zhang, Shichang, Zheng, Da, Zhang, Jiani, Zhu, Qi, song, Xiang, Adeshina, Soji, Faloutsos, Christos, Karypis, George, Sun, Yizhou
Text-rich graphs, prevalent in data mining contexts like e-commerce and academic graphs, consist of nodes with textual features linked by various relations. Traditional graph machine learning models, such as Graph Neural Networks (GNNs), excel in enc
Externí odkaz:
http://arxiv.org/abs/2406.11884
Autor:
Liang, Jiaming, Lei, Chuan, Qin, Xiao, Zhang, Jiani, Katsifodimos, Asterios, Faloutsos, Christos, Rangwala, Huzefa
Data-centric AI focuses on understanding and utilizing high-quality, relevant data in training machine learning (ML) models, thereby increasing the likelihood of producing accurate and useful results. Automatic feature augmentation, aiming to augment
Externí odkaz:
http://arxiv.org/abs/2406.09534
Autor:
Zheng, Da, Song, Xiang, Zhu, Qi, Zhang, Jian, Vasiloudis, Theodore, Ma, Runjie, Zhang, Houyu, Wang, Zichen, Adeshina, Soji, Nisa, Israt, Mottini, Alejandro, Cui, Qingjun, Rangwala, Huzefa, Zeng, Belinda, Faloutsos, Christos, Karypis, George
Publikováno v:
KDD 2024
Graph machine learning (GML) is effective in many business applications. However, making GML easy to use and applicable to industry applications with massive datasets remain challenging. We developed GraphStorm, which provides an end-to-end solution
Externí odkaz:
http://arxiv.org/abs/2406.06022
With the development of large models, watermarks are increasingly employed to assert copyright, verify authenticity, or monitor content distribution. As applications become more multimodal, the utility of watermarking techniques becomes even more cri
Externí odkaz:
http://arxiv.org/abs/2406.03728
How can we model arguments and their dynamics in online forum discussions? The meteoric rise of online forums presents researchers across different disciplines with an unprecedented opportunity: we have access to texts containing discourse between gr
Externí odkaz:
http://arxiv.org/abs/2405.15930
We propose WIBA, a novel framework and suite of methods that enable the comprehensive understanding of "What Is Being Argued" across contexts. Our approach develops a comprehensive framework that detects: (a) the existence, (b) the topic, and (c) the
Externí odkaz:
http://arxiv.org/abs/2405.00828
Autor:
Wang, Minjie, Gan, Quan, Wipf, David, Cai, Zhenkun, Li, Ning, Tang, Jianheng, Zhang, Yanlin, Zhang, Zizhao, Mao, Zunyao, Song, Yakun, Wang, Yanbo, Li, Jiahang, Zhang, Han, Yang, Guang, Qin, Xiao, Lei, Chuan, Zhang, Muhan, Zhang, Weinan, Faloutsos, Christos, Zhang, Zheng
Although RDBs store vast amounts of rich, informative data spread across interconnected tables, the progress of predictive machine learning models as applied to such tasks arguably falls well behind advances in other domains such as computer vision o
Externí odkaz:
http://arxiv.org/abs/2404.18209
Autor:
Park, Namyong, Rossi, Ryan, Wang, Xing, Simoulin, Antoine, Ahmed, Nesreen, Faloutsos, Christos
The choice of a graph learning (GL) model (i.e., a GL algorithm and its hyperparameter settings) has a significant impact on the performance of downstream tasks. However, selecting the right GL model becomes increasingly difficult and time consuming
Externí odkaz:
http://arxiv.org/abs/2404.01578