3 citations · 8 across the 10 of their papers we have counts for
8 papers · 1 filter
Predicting Drug-Drug Interactions from Heterogeneous Data: An Embedding Approach
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
Predicting and discovering drug-drug interactions (DDIs) using machine learning has been studied extensively. However, most of the approaches have focused on text data or textual r…
Non-Parametric Learning of Gaifman Models
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +1
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. These relat…
Non-Parametric Learning of Lifted Restricted Boltzmann Machines
Navdeep Kaur, Gautam Kunapuli, Sriraam Natarajan
We consider the problem of discriminatively learning restricted Boltzmann machines in the presence of relational data. Unlike previous approaches that employ a rule learner (for st…
Beyond Textual Data: Predicting Drug-Drug Interactions from Molecular Structure Images using Siamese Neural Networks
Devendra Singh Dhami, Siwen Yan, Gautam Kunapuli +2
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. Almost all o…
Neural Networks for Relational Data
Navdeep Kaur, Gautam Kunapuli, Saket Joshi +2
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 domain…
Knowledge-augmented Column Networks: Guiding Deep Learning with Advice
Mayukh Das, Devendra Singh Dhami, Yang Yu +2
Recently, deep models have had considerable success in several tasks, especially with low-level representations. However, effective learning from sparse noisy samples is a major ch…
Human-Guided Learning of Column Networks: Augmenting Deep Learning with Advice
Mayukh Das, Yang Yu, Devendra Singh Dhami +2
Recently, deep models have been successfully applied in several applications, especially with low-level representations. However, sparse, noisy samples and structured domains (with…
Structure Learning for Relational Logistic Regression: An Ensemble Approach
Nandini Ramanan, Gautam Kunapuli, Tushar Khot +5
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs are first-order formulae with associated weight…