Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Adaptive Hashing for Model Counting
Randomized hashing algorithms have seen recent success in providing bounds on the model count of a propositional formula. These methods repeatedly check the satisfiability of a formula subject to…
CEDR: Contextualized Embeddings for Document Ranking
Although considerable attention has been given to neural ranking architectures recently, far less attention has been paid to the term representations that are used as input to these models. In this…
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,…
Assisted Excitation of Activations: A Learning Technique to Improve Object Detectors
We present a simple and effective learning technique that significantly improves mAP of YOLO object detectors without compromising their speed. During network training, we carefully feed in…
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…
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…