no code implementations • 19 Mar 2021 • Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively.
no code implementations • 16 Mar 2021 • Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio Contardo, Amelia Regan, Julian Yarkony
We formulate the problem as a weighted set packing problem where the elements in consideration are items on the warehouse floor that can be picked up and delivered within specified time windows.
no code implementations • 8 Jun 2020 • Naveed Haghani, Jiaoyang Li, Sven Koenig, Gautam Kunapuli, Claudio Contardo, Julian Yarkony
We consider the problem of coordinating a fleet of robots in a warehouse so as to maximize the reward achieved within a time limit while respecting problem and robot specific constraints.
no code implementations • 13 Mar 2020 • Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
In this work, we propose a novel knowledge graph alignment technique based upon string edit distance that exploits the type information between entities and can find similarity between relations of any arity
no code implementations • 9 Jan 2020 • Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data.
no code implementations • 2 Jan 2020 • Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, Sriraam Natarajan
We consider the problem of structure learning for Gaifman models and learn relational features that can be used to derive feature representations from a knowledge base.
no code implementations • 14 Nov 2019 • Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli, David Page, Sriraam Natarajan
Predicting and discovering drug-drug interactions (DDIs) is an important problem and has been studied extensively both from medical and machine learning point of view.
1 code implementation • 28 Aug 2019 • Navdeep Kaur, Gautam Kunapuli, Saket Joshi, Kristian Kersting, Sriraam Natarajan
While deep networks have been enormously successful over the last decade, they rely on flat-feature vector representations, which makes them unsuitable for richly structured domains such as those arising in applications like social network analysis.
no code implementations • 31 May 2019 • Mayukh Das, Devendra Singh Dhami, Yang Yu, Gautam Kunapuli, Sriraam Natarajan
Recently, deep models have had considerable success in several tasks, especially with low-level representations.
no code implementations • ICLR 2019 • Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan
While extremely successful in several applications, especially with low-level representations; sparse, noisy samples and structured domains (with multiple objects and interactions) are some of the open challenges in most deep models.
no code implementations • 15 Apr 2019 • Mayukh Das, Yang Yu, Devendra Singh Dhami, Gautam Kunapuli, Sriraam Natarajan
Recently, deep models have been successfully applied in several applications, especially with low-level representations.
no code implementations • 6 Aug 2018 • Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan
We consider the problem of learning Relational Logistic Regression (RLR).
no code implementations • NeurIPS 2011 • Gautam Kunapuli, Richard Maclin, Jude W. Shavlik
Knowledge-based support vector machines (KBSVMs) incorporate advice from domain experts, which can improve generalization significantly.