Research - Papers
Explore a selection of our published work on a variety of key research challenges in AI.
Toward a Taxonomy and Computational Models of Abnormalities in Images
The human visual system can spot an abnormal image, and reason about what makes it strange. This task has not received enough attention in computer vision. In this paper we study various types of…
Toward Automatic Bootstrapping of Online Communities Using Decision-theoretic Optimization
Successful online communities (e.g., Wikipedia, Yelp, and StackOverflow) can produce valuable content. However, many communities fail in their initial stages. Starting an online community is…
Unsupervised Deep Embedding for Clustering Analysis
Clustering is central to many data-driven application domains and has been studied extensively in terms of distance functions and grouping algorithms. Relatively little work has focused on learning…
"What happens if..." Learning to Predict the Effect of Forces in Images
What happens if one pushes a cup sitting on a table toward the edge of the table? How about pushing a desk against a wall? In this paper, we study the problem of understanding the movements of…
What's in an Explanation? Characterizing Knowledge and Inference Requirements for Elementary Science Exams
QA systems have been making steady advances in the challenging elementary science exam domain. In this work, we develop an explanation-based analysis of knowledge and inference requirements, which…
XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
We propose two efficient approximations to standard convolutional neural networks: Binary-Weight-Networks and XNOR-Networks. In Binary-Weight-Networks, the filters are approximated with binary…
You Only Look Once: Unified, Real-Time Object Detection
We present YOLO, a new approach to object detection. Prior work on object detection repurposes classifiers to perform detection. Instead, we frame object detection as a regression problem to…
AI assisted ethics
The growing number of 'smart' instruments, those equipped with AI, has raised concerns because these instruments make autonomous decisions; that is, they act beyond the guidelines provided them by…
Neural AMR: Sequence-to-Sequence Models for Parsing and Generation
Sequence-to-sequence models have shown strong performance across a broad range of applications. However, their application to parsing and generating text using Abstract Meaning Representation (AMR)…
My Computer is an Honor Student — but how Intelligent is it? Standardized Tests as a Measure of AI
Given the well-known limitations of the Turing Test, there is a need for objective tests to both focus attention on, and measure progress towards, the goals of AI. In this paper we argue that…