Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Ontology-Aware Clinical Abstractive Summarization
Automatically generating accurate summaries from clinical reports could save a clinician's time, improve summary coverage, and reduce errors. We propose a sequence-to-sequence abstractive…
Exploiting Explicit Paths for Multi-hop Reading Comprehension
We propose a novel, path-based reasoning approach for the multi-hop reading comprehension task where a system needs to combine facts from multiple passages to answer a question. Although inspired by…
Quantifying Sex Bias in Clinical Studies at Scale With Automated Data Extraction
Importance: Analyses of female representation in clinical studies have been limited in scope and scale. Objective: To perform a large-scale analysis of global enrollment sex bias in clinical…
Cooperative Generator-Discriminator Networks for Abstractive Summarization with Narrative Flow
We introduce Cooperative Generator-Discriminator Networks (Co-opNet), a general framework for abstractive summarization with distinct modeling of the narrative flow in the output summary. Most…
Efficient Adaptation of Pretrained Transformers for Abstractive Summarization
Large-scale learning of transformer language models has yielded improvements on a variety of natural language understanding tasks. Whether they can be effectively adapted for summarization, however,…
Two Body Problem: Collaborative Visual Task Completion
Collaboration is a necessary skill to perform tasks that are beyond one agent's capabilities. Addressed extensively in both conventional and modern AI, multi-agent collaboration has often been…
ELASTIC: Improving CNNs by Instance Specific Scaling Policies
Scale variation has been a challenge from traditional to modern approaches in computer vision. Most solutions to scale issues have similar theme: a set of intuitive and manually designed policies…
Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning
Learning is an inherently continuous phenomenon. When humans learn a new task there is no explicit distinction between training and inference. After we learn a task, we keep learning about it while…
OK-VQA: A Visual Question Answering Benchmark Requiring External Knowledge
Visual Question Answering (VQA) in its ideal form lets us study reasoning in the joint space of vision and language and serves as a proxy for the AI task of scene understanding. However, most VQA…
ESPNetv2: A Light-weight, Power Efficient, and General Purpose Convolutional Neural Network
We introduce a light-weight, power efficient, and general purpose convolutional neural network, ESPNetv2 , for modeling visual and sequential data. Our network uses group point-wise and depth-wise…