106 citations · 256 across the 11 of their papers we have counts for
11 papers
Understanding Generative AI Content with Embedding Models
Max Vargas, Reilly Cannon, Andrew Engel +2
Constructing high-quality features is critical to any quantitative data analysis. While feature engineering was historically addressed by carefully hand-crafting data representatio…
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…
The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae
P. D. Aleo, K. Malanchev, S. Sharief +80
We present the Young Supernova Experiment Data Release 1 (YSE DR1), comprised of processed multi-color Pan-STARRS1 (PS1) griz and Zwicky Transient Facility (ZTF) gr photometry of 1…
Spectral Evolution and Invariance in Linear-width Neural Networks
Zhichao Wang, Andrew Engel, Anand Sarwate +2
We investigate the spectral properties of linear-width feed-forward neural networks, where the sample size is asymptotically proportional to network width. Empirically, we show tha…
TorchNTK: A Library for Calculation of Neural Tangent Kernels of PyTorch Models
Andrew Engel, Zhichao Wang, Anand D. Sarwate +2
We introduce torchNTK, a python library to calculate the empirical neural tangent kernel (NTK) of neural network models in the PyTorch framework. We provide an efficient method to…
The Young Supernova Experiment: Survey Goals, Overview, and Operations
D. O. Jones, R. J. Foley, G. Narayan +68
Time domain science has undergone a revolution over the past decade, with tens of thousands of new supernovae (SNe) discovered each year. However, several observational domains, in…
GHOST: Using Only Host Galaxy Information to Accurately Associate and Distinguish Supernovae
Alex Gagliano, Gautham Narayan, Andrew Engel +1
We present GHOST, a database of 16,175 spectroscopically classified supernovae and the properties of their host galaxies. We have developed a host galaxy association method using i…