8 citations · 12 across the 10 of their papers we have counts for
13 papers
Relational Neural Markov Random Fields
Yuqiao Chen, Sriraam Natarajan, Nicholas Ruozzi
Statistical Relational Learning (SRL) models have attracted significant attention due to their ability to model complex data while handling uncertainty. However, most of these mode…
Dynamic probabilistic logic models for effective abstractions in RL
Harsha Kokel, Arjun Manoharan, Sriraam Natarajan +2
State abstraction enables sample-efficient learning and better task transfer in complex reinforcement learning environments. Recently, we proposed RePReL (Kokel et al. 2021), a hie…
A Preliminary Approach for Learning Relational Policies for the Management of Critically Ill Children
Michael A. Skinner, Lakshmi Raman, Neel Shah +2
The increased use of electronic health records has made possible the automated extraction of medical policies from patient records to aid in the development of clinical decision su…
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…
User Friendly Automatic Construction of Background Knowledge: Mode Construction from ER Diagrams
Alexander L. Hayes, Mayukh Das, Phillip Odom +1
One of the key advantages of Inductive Logic Programming systems is the ability of the domain experts to provide background knowledge as modes that allow for efficient search throu…
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…