no code implementations • 7 May 2024 • Saurabh Varshneya, Antoine Ledent, Philipp Liznerski, Andriy Balinskyy, Purvanshi Mehta, Waleed Mustafa, Marius Kloft
Conventional machine learning methods are predominantly designed to predict outcomes based on a single data type.
no code implementations • 21 Feb 2024 • Mingyu Guan, Jack W. Stokes, Qinlong Luo, Fuchen Liu, Purvanshi Mehta, Elnaz Nouri, Taesoo Kim
In this paper, we present HetTree, a novel heterogeneous tree graph neural network that models both the graph structure and heterogeneous aspects in a scalable and effective manner.
1 code implementation • 15 Feb 2022 • Namyong Park, Fuchen Liu, Purvanshi Mehta, Dana Cristofor, Christos Faloutsos, Yuxiao Dong
How can we perform knowledge reasoning over temporal knowledge graphs (TKGs)?
1 code implementation • 26 May 2019 • Kumar Shridhar, Joonho Lee, Hideaki Hayashi, Purvanshi Mehta, Brian Kenji Iwana, Seokjun Kang, Seiichi Uchida, Sheraz Ahmed, Andreas Dengel
We show that ProbAct increases the classification accuracy by +2-3% compared to ReLU or other conventional activation functions on both original datasets and when datasets are reduced to 50% and 25% of the original size.
no code implementations • IJCNLP 2017 • Purvanshi Mehta, Pruthwik Mishra, Vinayak Athavale, Manish Shrivastava, Dipti Sharma
The worldstate and the query are processed separately in two different networks and finally, the networks are merged to predict the final operation.