Zobrazeno 1 - 10
of 191
pro vyhledávání: '"Khan, Zulqarnain"'
Inverse materials design has proven successful in accelerating novel material discovery. Many inverse materials design methods use unsupervised learning where a latent space is learned to offer a compact description of materials representations. A la
Externí odkaz:
http://arxiv.org/abs/2409.06740
Machine learning methods have significantly improved in their predictive capabilities, but at the same time they are becoming more complex and less transparent. As a result, explainers are often relied on to provide interpretability to these black-bo
Externí odkaz:
http://arxiv.org/abs/2206.12481
Autor:
Ihenetu, Stanley Chukwuemeka, Xu, Qiao, Khan, Zulqarnain Haider, Kazmi, Syed Shabi Ui Hassan, Ding, Jing, Sun, Qian, Li, Gang
Publikováno v:
In Environmental Research 1 October 2024 258
Autor:
Kazmi, Syed Shabi Ul Hassan, Xu, Qiao, Tayyab, Muhammad, Pastorino, Paolo, Barcelò, Damià, Yaseen, Zaher Mundher, Khan, Zulqarnain Haider, Li, Gang
Publikováno v:
In Environmental Pollution 1 September 2024 356
Autor:
Kazmi, Syed Shabi Ul Hassan, Tayyab, Muhammad, Pastorino, Paolo, Barcelò, Damià, Yaseen, Zaher Mundher, Grossart, Hans-Peter, Khan, Zulqarnain Haider, Li, Gang
Publikováno v:
In Journal of Hazardous Materials 5 July 2024 472
Autor:
Farnoosh, Amirreza, Rezaei, Behnaz, Sennesh, Eli Zachary, Khan, Zulqarnain, Dy, Jennifer, Satpute, Ajay, Hutchinson, J Benjamin, van de Meent, Jan-Willem, Ostadabbas, Sarah
We introduce deep Markov spatio-temporal factorization (DMSTF), a generative model for dynamical analysis of spatio-temporal data. Like other factor analysis methods, DMSTF approximates high dimensional data by a product between time dependent weight
Externí odkaz:
http://arxiv.org/abs/2003.09779
Autor:
Islam, Md Shafiqul, Zhu, Junhua, Xiao, Ling, Khan, Zulqarnain Haider, Saqib, Hafiz Sohaib Ahmed, Gao, Minling, Song, Zhengguo
Publikováno v:
In Chemosphere November 2023 342
Autor:
Shi, Xiaoling, Sadeghi, Pardis, Lobandi, Nader, Emam, Shadi, Seyed Abrishami, Seyed Mahdi, Martos-Repath, Isabel, Mani, Natesan, Nasrollahpour, Mehdi, Sun, William, Rones, Stav, Kwok, Joshua, Shah, Harsh, Charles, Joseph, Khan, Zulqarnain, Pagsuyoin, Sheree, Rojjanapinun, Akarapan, Liu, Ping, Chae, Jeongmin, Ferreira Da Costa, Maxime, Li, Jianxiu, Sun, Xin, Yang, Mengdi, Li, Jiahe, Dy, Jennifer, Wang, Jennifer, Luban, Jeremy, Chang, ChingWen, Finberg, Robert, Mitra, Urbashi, Cash, Sydney, Robbins, Gregory, Hodys, Cole, Lu, Hui, Wiegand, Patrick, Rieger, Robert, Sun, Nian X.
Publikováno v:
In Biosensors and Bioelectronics: X September 2023 14
We propose a deep learning approach for discovering kernels tailored to identifying clusters over sample data. Our neural network produces sample embeddings that are motivated by--and are at least as expressive as--spectral clustering. Our training o
Externí odkaz:
http://arxiv.org/abs/1908.03515
Autor:
Sennesh, Eli, Khan, Zulqarnain, Wang, Yiyu, Dy, Jennifer, Satpute, Ajay B., Hutchinson, J. Benjamin, van de Meent, Jan-Willem
Publikováno v:
Advances in Neural Information Processing Systems 34 (2020)
Neuroimaging studies produce gigabytes of spatio-temporal data for a small number of participants and stimuli. Rarely do researchers attempt to model and examine how individual participants vary from each other -- a question that should be addressabl
Externí odkaz:
http://arxiv.org/abs/1906.08901