Zobrazeno 1 - 10
of 42
pro vyhledávání: '"Iyer, Roshni"'
Autor:
Le, Khiem, Guo, Zhichun, Dong, Kaiwen, Huang, Xiaobao, Nan, Bozhao, Iyer, Roshni, Zhang, Xiangliang, Wiest, Olaf, Wang, Wei, Chawla, Nitesh V.
Large Language Models (LLMs) with their strong task-handling capabilities have shown remarkable advancements across a spectrum of fields, moving beyond natural language understanding. However, their proficiency within the chemistry domain remains res
Externí odkaz:
http://arxiv.org/abs/2406.06777
We present Bi-Level Attention-Based Relational Graph Convolutional Networks (BR-GCN), unique neural network architectures that utilize masked self-attentional layers with relational graph convolutions, to effectively operate on highly multi-relationa
Externí odkaz:
http://arxiv.org/abs/2404.09365
Recent graph neural networks (GNNs) with the attention mechanism have historically been limited to small-scale homogeneous graphs (HoGs). However, GNNs handling heterogeneous graphs (HeGs), which contain several entity and relation types, all have sh
Externí odkaz:
http://arxiv.org/abs/2304.11533
Two-view knowledge graphs (KGs) jointly represent two components: an ontology view for abstract and commonsense concepts, and an instance view for specific entities that are instantiated from ontological concepts. As such, these KGs contain heterogen
Externí odkaz:
http://arxiv.org/abs/2209.08767
Autor:
Guo, Zhichun, Guo, Kehan, Nan, Bozhao, Tian, Yijun, Iyer, Roshni G., Ma, Yihong, Wiest, Olaf, Zhang, Xiangliang, Wang, Wei, Zhang, Chuxu, Chawla, Nitesh V.
Molecular representation learning (MRL) is a key step to build the connection between machine learning and chemical science. In particular, it encodes molecules as numerical vectors preserving the molecular structures and features, on top of which th
Externí odkaz:
http://arxiv.org/abs/2207.04869
This research studies graph-based approaches for Answer Sentence Selection (AS2), an essential component for retrieval-based Question Answering (QA) systems. During offline learning, our model constructs a small-scale relevant training graph per ques
Externí odkaz:
http://arxiv.org/abs/2203.03549
Traditional code transformation structures, such as abstract syntax trees (ASTs), conteXtual flow graphs (XFGs), and more generally, compiler intermediate representations (IRs), may have limitations in extracting higher-order semantics from code. Whi
Externí odkaz:
http://arxiv.org/abs/2004.00768
Autor:
Letchford, Joshua, Epifanovskaya, Laura, Lakkaraju, Kiran, Armenta, Mika, Reddie, Andrew, Goldblum, Bethany L., Whetzel, Jon, Reinhardt, Jason, Balanaga, Vamshi, Chen, Andrew, Fabian, Nathan, Hingorani, Sheryl, Iyer, Roshni, Krishnan, Roshan, Laderman, Sarah, Lee, Manseok, Mohan, Janani, Nacht, Michael, Prakkamakul, Soravis, Sumner, Matthew, Tibbetts, Jake, Valdez, Allie, Zhang, Charlie
Publikováno v:
Military Operations Research, 2022 Jan 01. 27(2), 59-82.
Externí odkaz:
https://www.jstor.org/stable/27140356
Autor:
Iyer, Roshni, Nguyen, Tam, Padanilam, Dona, Xu, Cancan, Saha, Debabrata, Nguyen, Kytai T., Hong, Yi
Publikováno v:
In Journal of Controlled Release 10 May 2020 321:363-371
Autor:
Iyer, Roshni1 (AUTHOR) rosh25iyer@gmail.com, Ramachandramoorthy, Harish1,2 (AUTHOR) harish.ramachandramoor@mavs.uta.edu, Nguyen, Trinh1 (AUTHOR) txn1130@mavs.uta.edu, Xu, Cancan1 (AUTHOR) cancan.xu@mavs.uta.edu, Fu, Huikang1 (AUTHOR) huikang.fu@mavs.uta.edu, Kotadia, Tanviben3 (AUTHOR) tanviben.kotadia@mavs.uta.edu, Chen, Benjamin4 (AUTHOR) benjamin.chen@utsouthwestern.edu, Hong, Yi1,2 (AUTHOR) yihong@uta.edu, Saha, Debabrata1,4 (AUTHOR) debabrata.saha@utsouthwestern.edu, Nguyen, Kytai Truong1,2 (AUTHOR) debabrata.saha@utsouthwestern.edu
Publikováno v:
Pharmaceutics. Aug2022, Vol. 14 Issue 8, p1525-1525. 16p.