8 citations · 8 across the 5 of their papers we have counts for
5 papers
A Simple and Effective Method for Uncertainty Quantification and OOD Detection
Yaxin Ma, Benjamin Colburn, Jose C. Principe
Bayesian neural networks and deep ensemble methods have been proposed for uncertainty quantification; however, they are computationally intensive and require large storage. By util…
A Closed-Form Solution for Kernel Adaptive Filtering
Benjamin Colburn, Luis G. Sanchez Giraldo, Kan Li +1
Unlike the conventional kernel adaptive filtering (KAF) approach of using a fixed kernel to define the Reproducing Kernel Hilbert Space (RKHS), this paper embeds the statistics of…
An Alternate View on Optimal Filtering in an RKHS
Benjamin Colburn, Jose C. Principe, Luis G. Sanchez Giraldo
Kernel Adaptive Filtering (KAF) are mathematically principled methods which search for a function in a Reproducing Kernel Hilbert Space. While they work well for tasks such as time…
The Functional Wiener Filter
Benjamin Colburn, Luis G. Sanchez Giraldo, Jose C. Principe
This paper presents a close form solution in Reproducing Kernel Hilbert Space (RKHS) for the famed Wiener filter, which we called the functional Wiener filter(FWF). Instead of usin…
SignalTrain: Profiling Audio Compressors with Deep Neural Networks
Scott H. Hawley, Benjamin Colburn, Stylianos I. Mimilakis
In this work we present a data-driven approach for predicting the behavior of (i.e., profiling) a given non-linear audio signal processing effect (henceforth "audio effect"). Our o…