73 citations · 133 across the 13 of their papers we have counts for
4 papers · 1 filter
Efficient Fine-Tuning of BERT Models on the Edge
Danilo Vucetic, Mohammadreza Tayaranian, Maryam Ziaeefard +3
Resource-constrained devices are increasingly the deployment targets of machine learning applications. Static models, however, do not always suffice for dynamic environments. On-de…
Standard Deviation-Based Quantization for Deep Neural Networks
Amir Ardakani, Arash Ardakani, Brett Meyer +2
Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by ex…
Surprisal-Triggered Conditional Computation with Neural Networks
Loren Lugosch, Derek Nowrouzezahrai, Brett H. Meyer
Autoregressive neural network models have been used successfully for sequence generation, feature extraction, and hypothesis scoring. This paper presents yet another use for these…
Learning Recurrent Binary/Ternary Weights
Arash Ardakani, Zhengyun Ji, Sean C. Smithson +2
Recurrent neural networks (RNNs) have shown excellent performance in processing sequence data. However, they are both complex and memory intensive due to their recursive nature. Th…