133 citations · 499 across the 17 of their papers we have counts for
17 papers
Vector Embeddings by Sequence Similarity and Context for Improved Compression, Similarity Search, Clustering, Organization, and Manipulation of cDNA Libraries
Daniel H. Um, David A. Knowles, Gail E. Kaiser
This paper demonstrates the utility of organized numerical representations of genes in research involving flat string gene formats (i.e., FASTA/FASTQ5). FASTA/FASTQ files have seve…
Faithful Heteroscedastic Regression with Neural Networks
Andrew Stirn, Hans-Hermann Wessels, Megan Schertzer +3
Heteroscedastic regression models a Gaussian variable's mean and variance as a function of covariates. Parametric methods that employ neural networks for these parameter maps can c…
Active Learning in CNNs via Expected Improvement Maximization
Udai G. Nagpal, David A Knowles
Deep learning models such as Convolutional Neural Networks (CNNs) have demonstrated high levels of effectiveness in a variety of domains, including computer vision and more recentl…
Variational Variance: Simple, Reliable, Calibrated Heteroscedastic Noise Variance Parameterization
Andrew Stirn, David A. Knowles
Brittle optimization has been observed to adversely impact model likelihoods for regression and VAEs when simultaneously fitting neural network mappings from a (random) variable on…
A New Distribution on the Simplex with Auto-Encoding Applications
Andrew Stirn, Tony Jebara, David A Knowles
We construct a new distribution for the simplex using the Kumaraswamy distribution and an ordered stick-breaking process. We explore and develop the theoretical properties of this…
Stochastic gradient variational Bayes for gamma approximating distributions
David A. Knowles
While stochastic variational inference is relatively well known for scaling inference in Bayesian probabilistic models, related methods also offer ways to circumnavigate the approx…