7 citations · 7 across the 4 of their papers we have counts for
6 papers
GlideNet: Global, Local and Intrinsic based Dense Embedding NETwork for Multi-category Attributes Prediction
Kareem Metwaly, Aerin Kim, Elliot Branson +1
Attaching attributes (such as color, shape, state, action) to object categories is an important computer vision problem. Attribute prediction has seen exciting recent progress and…
On The State of Data In Computer Vision: Human Annotations Remain Indispensable for Developing Deep Learning Models
Zeyad Emam, Andrew Kondrich, Sasha Harrison +4
High-quality labeled datasets play a crucial role in fueling the development of machine learning (ML), and in particular the development of deep learning (DL). However, since the e…
Breaking hypothesis testing for failure rates
Rohit Pandey, Yingnong Dang, Gil Lapid Shafriri +2
We describe the utility of point processes and failure rates and the most common point process for modeling failure rates, the Poisson point process. Next, we describe the uniforml…
Annual Interruption Rate as a KPI, its measurement and comparison
Rohit Pandey, Yingnong Dang, Ali Vira +3
This article is divided into two chapters. The first chapter describes the failure rate as a KPI and studies its properties. The second one goes over ways to compare this KPI acros…
Optimizing Waiting Thresholds Within A State Machine
Rohit Pandey, Yifan Chang, Cameron White +4
Azure (the cloud service provided by Microsoft) is composed of physical computing units which are called nodes. These nodes are controlled by a software component called Fabric Con…
Stochastic Answer Networks for SQuAD 2.0
Xiaodong Liu, Wei Li, Yuwei Fang +3
This paper presents an extension of the Stochastic Answer Network (SAN), one of the state-of-the-art machine reading comprehension models, to be able to judge whether a question is…