no code implementations • 7 Feb 2023 • Akshita Jha, Adithya Samavedhi, Vineeth Rakesh, Jaideep Chandrashekar, Chandan K. Reddy
Firstly, the performance gain provided by transformer-based models comes at a steep cost - both in terms of the required training time and the resource (memory and energy) consumption.
no code implementations • 2 Dec 2022 • Xiaoting Li, Yuhang Wu, Vineeth Rakesh, Yusan Lin, Hao Yang, Fei Wang
Graph neural networks have achieved significant success in representation learning.
1 code implementation • 20 Aug 2021 • Akshita Jha, Vineeth Rakesh, Jaideep Chandrashekar, Adithya Samavedhi, Chandan K. Reddy
When handling such long documents, there are three primary challenges: (i) the presence of different contexts for the same word throughout the document, (ii) small sections of contextually similar text between two documents, but dissimilar text in the remaining parts (this defies the basic understanding of "similarity"), and (iii) the coarse nature of a single global similarity measure which fails to capture the heterogeneity of the document content.
1 code implementation • 2 Apr 2021 • Vineeth Rakesh, Swayambhoo Jain
By performing a comprehensive set of experiments, we show that Bayesian neural networks are more efficient than ensemble based techniques in capturing uncertainty.
1 code implementation • 30 May 2019 • Emanuele Bugliarello, Swayambhoo Jain, Vineeth Rakesh
We tackle this challenge by using a two-fold approach: first, we transform this task into a constrained matrix completion problem with entries bounded in the unit interval [0, 1]; second, we propose two novel matrix factorization models that leverage our knowledge of the VFX environment.
1 code implementation • 9 Aug 2018 • Vineeth Rakesh, Ruocheng Guo, Raha Moraffah, Nitin Agarwal, Huan Liu
Modeling spillover effects from observational data is an important problem in economics, business, and other fields of research.