Publications

ImageNet Large Scale Visual Recognition Challenge

The rise of graphene

Fully convolutional networks for semantic segmentation

Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, ex...

QIIME allows analysis of high-throughput community sequencing data

SciPy 1.0: fundamental algorithms for scientific computing in Python

Glove: Global Vectors for Word Representation

Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the o...

Electrochemical Photolysis of Water at a Semiconductor Electrode

Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation

Object detection performance, as measured on the canonical PASCAL VOC dataset, has plateaued in the last few years. The best-performing methods are complex ensemble systems that...

Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: ...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to bec...

Institutions, Institutional Change and Economic Performance

Continuing his groundbreaking analysis of economic structures, Douglass North develops an analytical framework for explaining the ways in which institutions and institutional ch...

Society and the Adolescent Self-Image

Over 5,000 high-school students of different social, religious, and national backgrounds were studied to show the effects of family experience, neighborhoods, minority groups, e...