568 citations · 2.2k across the 54 of their papers we have counts for
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Towards calibrated and scalable uncertainty representations for neural networks
Nabeel Seedat, Christopher Kanan
For many applications it is critical to know the uncertainty of a neural network's predictions. While a variety of neural network parameter estimation methods have been proposed fo…
AeroRIT: A New Scene for Hyperspectral Image Analysis
Aneesh Rangnekar, Nilay Mokashi, Emmett Ientilucci +2
We investigate applying convolutional neural network (CNN) architecture to facilitate aerial hyperspectral scene understanding and present a new hyperspectral dataset-AeroRIT-that…
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?
Ryne Roady, Tyler L. Hayes, Ronald Kemker +2
Supervised classification methods often assume the train and test data distributions are the same and that all classes in the test set are present in the training set. However, dep…
REMIND Your Neural Network to Prevent Catastrophic Forgetting
Tyler L. Hayes, Kushal Kafle, Robik Shrestha +2
People learn throughout life. However, incrementally updating conventional neural networks leads to catastrophic forgetting. A common remedy is replay, which is inspired by how the…
RITnet: Real-time Semantic Segmentation of the Eye for Gaze Tracking
Aayush K. Chaudhary, Rakshit Kothari, Manoj Acharya +6
Accurate eye segmentation can improve eye-gaze estimation and support interactive computing based on visual attention; however, existing eye segmentation methods suffer from issues…
Lifelong Machine Learning with Deep Streaming Linear Discriminant Analysis
Tyler L. Hayes, Christopher Kanan
When an agent acquires new information, ideally it would immediately be capable of using that information to understand its environment. This is not possible using conventional dee…