Zobrazeno 1 - 10
of 17
pro vyhledávání: '"Kshitij Tayal"'
Publikováno v:
Environmental Research Letters, Vol 19, Iss 10, p 104009 (2024)
Accurate streamflow prediction is crucial to understand climate impacts on water resources and develop effective adaption strategies. A global long short-term memory (LSTM) model, using data from multiple basins, can enhance streamflow prediction, ye
Externí odkaz:
https://doaj.org/article/4e7d7692fc0b43949f8267d00099750a
Publikováno v:
2022 IEEE International Conference on Data Mining (ICDM).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6184a67ff338e1140b3e9ea86b1310f9
https://doi.org/10.1109/icdm54844.2022.00026
https://doi.org/10.1109/icdm54844.2022.00026
Autor:
Varun Ratnakar, Deborah Khider, Yijun Lin, Maximiliano Osorio, Daniel Garijo, Zeya Zhang, Scott D. Peckham, Rajiv Mayani, Jay Pujara, Rafael Ferreira da Silva, Dan Feldman, Yao-Yi Chiang, Daniel Hardesty-Lewis, Basel Shbita, Yuning Shi, Craig A. Knoblock, Lele Shu, Michael Steinbach, Maria Stoica, Kshitij Tayal, Lissa Pearson, Kelly M. Cobourn, Ankush Khandelwal, Vipin Kumar, Yolanda Gil, Lorne Leonard, Suzanne A. Pierce, Binh Vu, Armen R. Kemanian, Shaoming Xu, Ewa Deelman, Christopher J. Duffy, Minh Pham, Hernán Vargas, Hayley Song
Publikováno v:
ACM Transactions on Interactive Intelligent Systems. 11:1-49
Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect wat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3e2c76a809f53e3d27278d46997e232d
https://doi.org/10.1145/3453172
https://doi.org/10.1145/3453172
Autor:
Xiang Li, Ankush Khandelwal, Xiaowei Jia, Kelly Cutler, Rahul Ghosh, Arvind Renganathan, Shaoming Xu, Kshitij Tayal, J L Nieber, Christopher J Duffy, Michael Steinbach, Vipin Kumar
Publikováno v:
Water Resources Research. 58
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f0f38d6ddc8375f2cb395c4ecfcaa780
https://doi.org/10.1029/2021wr031794
https://doi.org/10.1029/2021wr031794
Autor:
Rahul Ghosh, Arvind Renganathan, Kshitij Tayal, Xiang Li, Ankush Khandelwal, Xiaowei Jia, Christopher Duffy, John Nieber, Vipin Kumar
Machine Learning is beginning to provide state-of-the-art performance in a range of environmental applications such as streamflow prediction in a hydrologic basin. However, building accurate broad-scale models for streamflow remains challenging in pr
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::547d8ed76a0fc688d4cc0a4a2325c415
Publikováno v:
Experts@Minnesota
The existing iterative and data-driven methods fail to solve phase retrieval due to the intrinsic problem symmetries. We propose two end-to-end learning methods that break the barrier and work in a new regime.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9973f6d894b428854c711138faf71eac
https://doi.org/10.1364/cosi.2021.cth4a.4
https://doi.org/10.1364/cosi.2021.cth4a.4
Publikováno v:
Experts@Minnesota
In many physical systems, inputs related by intrinsic system symmetries are mapped to the same output. When inverting such systems, i.e., solving the associated inverse problems, there is no unique solution. This causes fundamental difficulties for d
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6a4bd45bdce64f909f51a91f35fb4c24
http://arxiv.org/abs/2003.09077
http://arxiv.org/abs/2003.09077
Publikováno v:
COLING (Industry)
Text classification is a fundamental problem, and recently, deep neural networks (DNN) have shown promising results in many natural language tasks. However, their human-level performance relies on high-quality annotations, which are time-consuming an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a9f1e222406b23467c7cfca31dd5a47c
https://doi.org/10.18653/v1/2020.coling-industry.19
https://doi.org/10.18653/v1/2020.coling-industry.19
Publikováno v:
COLING (Industry)
Short text classification is a fundamental problem in natural language processing, social network analysis, and e-commerce. The lack of structure in short text sequences limits the success of popular NLP methods based on deep learning. Simpler method
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::0205ac4adc576e3b0046815d46000848
https://doi.org/10.18653/v1/2020.coling-industry.22
https://doi.org/10.18653/v1/2020.coling-industry.22