Zobrazeno 1 - 10
of 235
pro vyhledávání: '"Candan, K. Selçuk"'
Autor:
Kapkiç, Ahmet, Mandal, Pratanu, Wan, Shu, Sheth, Paras, Gorantla, Abhinav, Choi, Yoonhyuk, Liu, Huan, Candan, K. Selçuk
While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal re
Externí odkaz:
http://arxiv.org/abs/2409.08419
Autor:
Azad, Fahim Tasneema, Anton, Javier Redondo, Mitra, Shubhodeep, Singh, Fateh, Behrens, Hans, Li, Mao-Lin, Arslan, Bilgehan, Candan, K. Selçuk, Sapino, Maria Luisa
Many socio-economical critical domains (such as sustainability, public health, and disasters) are characterized by highly complex and dynamic systems, requiring data and model-driven simulations to support decision-making. Due to a large number of un
Externí odkaz:
http://arxiv.org/abs/2407.14571
Wetlands are important to communities, offering benefits ranging from water purification, and flood protection to recreation and tourism. Therefore, identifying and prioritizing potential wetland areas is a critical decision problem. While data-drive
Externí odkaz:
http://arxiv.org/abs/2406.05578
Autor:
McAlister, John S., Blum, Michael J., Bromberg, Yana, Fefferman, Nina H., He, Qiang, Lofgren, Eric, Miller, Debra L., Schreiner, Courtney, Candan, K. Selcuk, Szabo-Rogers, Heather, Reed, J. Michael
The built environment provides an excellent setting for interdisciplinary research on the dynamics of microbial communities. The system is simplified compared to many natural settings, and to some extent the entire environment can be manipulated, fro
Externí odkaz:
http://arxiv.org/abs/2405.02593
Discovering causal relationships in complex socio-behavioral systems is challenging but essential for informed decision-making. We present Upload, PREprocess, Visualize, and Evaluate (UPREVE), a user-friendly web-based graphical user interface (GUI)
Externí odkaz:
http://arxiv.org/abs/2307.13757
Publikováno v:
2024 IEEE 40th International Conference on Data Engineering (ICDE)
Storing tabular data to balance storage and query efficiency is a long-standing research question in the database community. In this work, we argue and show that a novel DeepMapping abstraction, which relies on the impressive memorization capabilitie
Externí odkaz:
http://arxiv.org/abs/2307.05861
Most neural network-based classifiers extract features using several hidden layers and make predictions at the output layer by utilizing these extracted features. We observe that not all features are equally pronounced in all classes; we call such fe
Externí odkaz:
http://arxiv.org/abs/2211.10609