18 citations · 35 across the 10 of their papers we have counts for
10 papers
Improving the Accuracy-Robustness Trade-Off of Classifiers via Adaptive Smoothing
Yatong Bai, Brendon G. Anderson, Aerin Kim +1
While prior research has proposed a plethora of methods that build neural classifiers robust against adversarial robustness, practitioners are still reluctant to adopt them due to…
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…
CAR -- Cityscapes Attributes Recognition A Multi-category Attributes Dataset for Autonomous Vehicles
Kareem Metwaly, Aerin Kim, Elliot Branson +1
Self-driving vehicles are the future of transportation. With current advancements in this field, the world is getting closer to safe roads with almost zero probability of having ac…
Natural Adversarial Objects
Felix Lau, Nishant Subramani, Sasha Harrison +3
Although state-of-the-art object detection methods have shown compelling performance, models often are not robust to adversarial attacks and out-of-distribution data. We introduce…
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…
Evaluating Deep Neural Networks Trained on Clinical Images in Dermatology with the Fitzpatrick 17k Dataset
Matthew Groh, Caleb Harris, Luis Soenksen +5
How does the accuracy of deep neural network models trained to classify clinical images of skin conditions vary across skin color? While recent studies demonstrate computer vision…
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…