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Research - Papers

Explore a selection of our published work on a variety of key research challenges in AI.

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Ontology-Aware Clinical Abstractive Summarization

Sean MacAvaneySajad SotudehArman CohanRoss W. Filice
2019
SIGIR

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

Souvik KunduTushar KhotAshish SabharwalPeter Clark
2019
ACL

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

Sergey FeldmanWaleed AmmarKyle LoOren Etzioni
2019
JAMA

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

Saadia GabrielAntoine BosselutAri HoltzmanYejin Choi
2019
arXiv

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

Andrew Pau HoangAntoine BosselutAsli ÇelikyilmazYejin Choi
2019
arXiv

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

Mohammad Mahdi DerakhshaniSaeed MasoudniaAmir Hossein ShakerBabak N. Araabi
2019
CVPR

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

Huiyu WangAniruddha KembhaviAli FarhadiMohammad Rastegari
2019
CVPR

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

Sachin MehtaMohammad RastegariLinda ShapiroHannaneh Hajishirzi
2019
CVPR

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… 

From Recognition to Cognition: Visual Commonsense Reasoning

Rowan ZellersYonatan BiskAli FarhadiYejin Choi
2019
CVPR

Visual understanding goes well beyond object recognition. With one glance at an image, we can effortlessly imagine the world beyond the pixels: for instance, we can infer people’s actions, goals,… 

Learning to Learn How to Learn: Self-Adaptive Visual Navigation Using Meta-Learning

Mitchell WortsmanKiana EhsaniMohammad RastegariRoozbeh Mottaghi
2019
CVPR

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…