133 citations · 278 across the 25 of their papers we have counts for
17 papers · 1 filter
Efficient Malware Analysis Using Metric Embeddings
Ethan M. Rudd, David Krisiloff, Scott Coull +3
In this paper, we explore the use of metric learning to embed Windows PE files in a low-dimensional vector space for downstream use in a variety of applications, including malware…
Lempel-Ziv Networks
Rebecca Saul, Mohammad Mahmudul Alam, John Hurwitz +3
Sequence processing has long been a central area of machine learning research. Recurrent neural nets have been successful in processing sequences for a number of tasks; however, th…
Improving Out-of-Distribution Detection via Epistemic Uncertainty Adversarial Training
Derek Everett, Andre T. Nguyen, Luke E. Richards +1
The quantification of uncertainty is important for the adoption of machine learning, especially to reject out-of-distribution (OOD) data back to human experts for review. Yet progr…
A Siren Song of Open Source Reproducibility
Edward Raff, Andrew L. Farris
As reproducibility becomes a greater concern, conferences have largely converged to a strategy of asking reviewers to indicate whether code was attached to a submission. This is pa…
Out of Distribution Data Detection Using Dropout Bayesian Neural Networks
Andre T. Nguyen, Fred Lu, Gary Lopez Munoz +3
We explore the utility of information contained within a dropout based Bayesian neural network (BNN) for the task of detecting out of distribution (OOD) data. We first show how pre…
Continuously Generalized Ordinal Regression for Linear and Deep Models
Fred Lu, Francis Ferraro, Edward Raff
Ordinal regression is a classification task where classes have an order and prediction error increases the further the predicted class is from the true class. The standard approach…