New York University

About: New York University is a education organization based out in New York, New York, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 72380 authors who have published 165545 publications receiving 8334030 citations. The organization is also known as: NYU & University of the City of New York.

Depth Map Prediction from a Single Image using a Multi-Scale Deep Network

Abstract: Predicting depth is an essential component in understanding the 3D geometry of a scene. While for stereo images local correspondence suffices for estimation, finding depth relations from a single image is less straightforward, requiring integration of both global and local information from various cues. Moreover, the task is inherently ambiguous, with a large source of uncertainty coming from the overall scale. In this paper, we present a new method that addresses this task by employing two deep network stacks: one that makes a coarse global prediction based on the entire image, and another that refines this prediction locally. We also apply a scale-invariant error to help measure depth relations rather than scale. By leveraging the raw datasets as large sources of training data, our method achieves state-of-the-art results on both NYU Depth and KITTI, and matches detailed depth boundaries without the need for superpixelation.

The equivalence of weighted kappa and the intraclass correlation coefficient as measures of reliability.

Abstract: or weighted kappa (Spitzer, Cohen, Fleiss and Endicott, 1967; Cohen, 1968a). Kappa is the proportion of agreement corrected for chance, and scaled to vary from -1 to +1 so that a negative value indicates poorer than chance agreement, zero indicates exactly chance agreement, and a positive value indicates better than chance agreement. A value of unity indicates perfect agreement. The use of kappa implicitly assumes that all disagreements are equally serious. When the investigator can specify the relative seriousness of each kind of disagreement, he may employ weighted kappa, the proportion of weighted agreement corrected for chance. For measuring the reliability of quantitative scales, the product-moment and intraclass correlation coefficients are widely

Visualizing and Understanding Convolutional Networks

Abstract: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we address both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. We also perform an ablation study to discover the performance contribution from different model layers. This enables us to find model architectures that outperform Krizhevsky \etal on the ImageNet classification benchmark. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.

Inside the Black Box: The Credit Channel of Monetary Policy Transmission

Abstract: The 'credit channel' theory of monetary policy transmission holds that informational frictions in credit markets worsen during tight- money periods. The resulting increase in the external finance premium--the difference in cost between internal and external funds-- enhances the effects of monetary policy on the real economy. We document the responses of GDP and its components to monetary policy shocks and describe how the credit channel helps explain the facts. We discuss two main components of this mechanism, the balance-sheet channel and the bank lending channel. We argue that forecasting exercises using credit aggregates are not valid tests of this theory.