3 citations · 4 across the 2 of their papers we have counts for
2 papers
cs.LG2023★ 1 cited
End-to-end codesign of Hessian-aware quantized neural networks for FPGAs and ASICs
Javier Campos, Zhen Dong, Javier Duarte +4
We develop an end-to-end workflow for the training and implementation of co-designed neural networks (NNs) for efficient field-programmable gate array (FPGA) and application-specif…
cs.LG2022★ 3 cited
Adaptive Self-supervision Algorithms for Physics-informed Neural Networks
Shashank Subramanian, Robert M. Kirby, Michael W. Mahoney +1
Physics-informed neural networks (PINNs) incorporate physical knowledge from the problem domain as a soft constraint on the loss function, but recent work has shown that this can l…