118 citations · 1.4k across the 52 of their papers we have counts for
3 papers · 1 filter
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…
Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
Sehoon Kim, Amir Gholami, Albert Shaw +5
The recently proposed Conformer model has become the de facto backbone model for various downstream speech tasks based on its hybrid attention-convolution architecture that capture…
A Fast Post-Training Pruning Framework for Transformers
Woosuk Kwon, Sehoon Kim, Michael W. Mahoney +3
Pruning is an effective way to reduce the huge inference cost of Transformer models. However, prior work on pruning Transformers requires retraining the models. This can add high t…