22 citations · 107 across the 37 of their papers we have counts for
10 papers · 1 filter
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…
Extreme Acceleration of Graph Neural Network-based Prediction Models for Quantum Chemistry
Hatem Helal, Jesun Firoz, Jenna Bilbrey +5
Molecular property calculations are the bedrock of chemical physics. High-fidelity \textit{ab initio} modeling techniques for computing the molecular properties can be prohibitivel…
Reducing Down(stream)time: Pretraining Molecular GNNs using Heterogeneous AI Accelerators
Jenna A. Bilbrey, Kristina M. Herman, Henry Sprueill +6
The demonstrated success of transfer learning has popularized approaches that involve pretraining models from massive data sources and subsequent finetuning towards a specific task…
A Unification Framework for Euclidean and Hyperbolic Graph Neural Networks
Mehrdad Khatir, Nurendra Choudhary, Sutanay Choudhury +2
Hyperbolic neural networks can effectively capture the inherent hierarchy of graph datasets, and consequently a powerful choice of GNNs. However, they entangle multiple incongruent…
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…
Benchmarking Deep Graph Generative Models for Optimizing New Drug Molecules for COVID-19
Logan Ward, Jenna A. Bilbrey, Sutanay Choudhury +2
Design of new drug compounds with target properties is a key area of research in generative modeling. We present a small drug molecule design pipeline based on graph-generative mod…