363 citations · 485 across the 18 of their papers we have counts for
6 papers · 1 filter
An image-driven machine learning approach to kinetic modeling of a discontinuous precipitation reaction
Elizabeth Kautz, Wufei Ma, Saumyadeep Jana +4
Micrograph quantification is an essential component of several materials science studies. Machine learning methods, in particular convolutional neural networks, have previously dem…
Deep density ratio estimation for change point detection
Haidar Khan, Lara Marcuse, Bülent Yener
In this work, we propose new objective functions to train deep neural network based density ratio estimators and apply it to a change point detection problem. Existing methods use…
Thwarting finite difference adversarial attacks with output randomization
Haidar Khan, Daniel Park, Azer Khan +1
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposit…
Generation & Evaluation of Adversarial Examples for Malware Obfuscation
Daniel Park, Haidar Khan, Bülent Yener
There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to advers…
Quantifying error contributions of computational steps, algorithms and hyperparameter choices in image classification pipelines
Aritra Chowdhury, Malik Magdin-Ismail, Bulent Yener
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for perfor…
Quantifying contribution and propagation of error from computational steps, algorithms and hyperparameter choices in image classification pipelines
Aritra Chowdhury, Malik Magdon-Ismail, Bulent Yener
Data science relies on pipelines that are organized in the form of interdependent computational steps. Each step consists of various candidate algorithms that maybe used for perfor…