571 citations · 2.8k across the 60 of their papers we have counts for
4 papers · 1 filter
Efficient Fine-Tuning of Compressed Language Models with Learners
Danilo Vucetic, Mohammadreza Tayaranian, Maryam Ziaeefard +3
Fine-tuning BERT-based models is resource-intensive in memory, computation, and time. While many prior works aim to improve inference efficiency via compression techniques, e.g., p…
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…
GRAND for Rayleigh Fading Channels
Syed Mohsin Abbas, Marwan Jalaleddine, Warren J. Gross
Guessing Random Additive Noise Decoding (GRAND) is a code-agnostic decoding technique for short-length and high-rate channel codes. GRAND tries to guess the channel noise by genera…
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…