no code implementations • 29 Nov 2023 • Max Milkert, David Hyde, Forrest Laine
To address this issue, we introduce a novel training strategy: we first reparameterize the network weights in a manner that forces an exponential number of activation patterns to manifest.
no code implementations • 24 Apr 2023 • Harsh Vardhan, David Hyde, Umesh Timalsina, Peter Volgyesi, Janos Sztipanovits
In this work, we leverage recent advances in optimization and artificial intelligence (AI) to explore both of these potential approaches, in the context of designing an optimal unmanned underwater vehicle (UUV) hull.
no code implementations • 22 May 2022 • Ayano Kaneda, Osman Akar, Jingyu Chen, Victoria Kala, David Hyde, Joseph Teran
We present a novel deep learning approach to approximate the solution of large, sparse, symmetric, positive-definite linear systems of equations.
no code implementations • 7 Dec 2018 • Michael Bao, David Hyde, Xinru Hua, Ronald Fedkiw
It is well known that popular optimization techniques can lead to overfitting or even a lack of convergence altogether; thus, practitioners often utilize ad hoc regularization terms added to the energy functional.