70 citations · 100 across the 13 of their papers we have counts for
5 papers · 1 filter
Deep Speech Synthesis from Multimodal Articulatory Representations
Peter Wu, Bohan Yu, Kevin Scheck +6
The amount of articulatory data available for training deep learning models is much less compared to acoustic speech data. In order to improve articulatory-to-acoustic synthesis pe…
The Importance of Being Scalable: Improving the Speed and Accuracy of Neural Network Interatomic Potentials Across Chemical Domains
Eric Qu, Aditi S. Krishnapriyan
Scaling has been critical in improving model performance and generalization in machine learning. It involves how a model's performance changes with increases in model size or input…
Physics-Informed Heterogeneous Graph Neural Networks for DC Blocker Placement
Hongwei Jin, Prasanna Balaprakash, Allen Zou +5
The threat of geomagnetic disturbances (GMDs) to the reliable operation of the bulk energy system has spurred the development of effective strategies for mitigating their impacts.…
Stability-Aware Training of Machine Learning Force Fields with Differentiable Boltzmann Estimators
Sanjeev Raja, Ishan Amin, Fabian Pedregosa +1
Machine learning force fields (MLFFs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations, li…
Enabling Efficient Equivariant Operations in the Fourier Basis via Gaunt Tensor Products
Shengjie Luo, Tianlang Chen, Aditi S. Krishnapriyan
Developing equivariant neural networks for the E(3) group plays an important role in modeling 3D data across real-world applications. Enforcing this equivariance primarily involves…