106 citations · 256 across the 11 of their papers we have counts for
5 papers · 1 filter
Efficient kernel surrogates for neural network-based regression
Saad Qadeer, Andrew Engel, Amanda Howard +4
Despite their immense promise in performing a variety of learning tasks, a theoretical understanding of the limitations of Deep Neural Networks (DNNs) has so far eluded practitione…
Foundation Model's Embedded Representations May Detect Distribution Shift
Max Vargas, Adam Tsou, Andrew Engel +1
Sampling biases can cause distribution shifts between train and test datasets for supervised learning tasks, obscuring our ability to understand the generalization capacity of a mo…
Exploring Learned Representations of Neural Networks with Principal Component Analysis
Amit Harlev, Andrew Engel, Panos Stinis +1
Understanding feature representation for deep neural networks (DNNs) remains an open question within the general field of explainable AI. We use principal component analysis (PCA)…
Faithful and Efficient Explanations for Neural Networks via Neural Tangent Kernel Surrogate Models
Andrew Engel, Zhichao Wang, Natalie S. Frank +4
A recent trend in explainable AI research has focused on surrogate modeling, where neural networks are approximated as simpler ML algorithms such as kernel machines. A second trend…
YSE-PZ: A Transient Survey Management Platform that Empowers the Human-in-the-Loop
D. A. Coulter, D. O. Jones, P. McGill +19
The modern study of astrophysical transients has been transformed by an exponentially growing volume of data. Within the last decade, the transient discovery rate has increased by…