Publications (39)
Granite Vision: a lightweight, open-source multimodal model for enterprise Intelligence
Granite Vision Team, Leonid Karlinsky, Assaf Arbelle +60
We introduce Granite Vision, a lightweight large language model with vision capabilities, specifically designed to excel in enterprise use cases, particularly in visual document un…
Verifiably Safe Exploration for End-to-End Reinforcement Learning
Nathan Hunt, Nathan Fulton, Sara Magliacane +3
Deploying deep reinforcement learning in safety-critical settings requires developing algorithms that obey hard constraints during exploration. This paper contributes a first appro…
Guaranteeing Conservation Laws with Projection in Physics-Informed Neural Networks
Anthony Baez, Wang Zhang, Ziwen Ma +3
Physics-informed neural networks (PINNs) incorporate physical laws into their training to efficiently solve partial differential equations (PDEs) with minimal data. However, PINNs…
On observability and optimal gain design for distributed linear filtering and prediction
Subhro Das
This paper presents a new approach to distributed linear filtering and prediction. The problem under consideration consists of a random dynamical system observed by a multi-agent n…
Learning Patient Engagement in Care Management: Performance vs. Interpretability
Subhro Das, Chandramouli Maduri, Ching-Hua Chen +1
The health outcomes of high-need patients can be substantially influenced by the degree of patient engagement in their own care. The role of care managers includes that of enrollin…
Model adaptation and unsupervised learning with non-stationary batch data under smooth concept drift
Subhro Das, Prasanth Lade, Soundar Srinivasan
Most predictive models assume that training and test data are generated from a stationary process. However, this assumption does not hold true in practice. In this paper, we consid…