34 citations · 83 across the 9 of their papers we have counts for
13 papers
Automatic Pruning of Fine-tuning Datasets for Transformer-based Language Models
Mohammadreza Tayaranian, Seyyed Hasan Mozafari, Brett H. Meyer +2
Transformer-based language models have shown state-of-the-art performance on a variety of natural language understanding tasks. To achieve this performance, these models are first…
Faster Inference of Integer SWIN Transformer by Removing the GELU Activation
Mohammadreza Tayaranian, Seyyed Hasan Mozafari, James J. Clark +2
SWIN transformer is a prominent vision transformer model that has state-of-the-art accuracy in image classification tasks. Despite this success, its unique architecture causes slow…
AdCorDA: Classifier Refinement via Adversarial Correction and Domain Adaptation
Lulan Shen, Ali Edalati, Brett Meyer +2
This paper describes a simple yet effective technique for refining a pretrained classifier network. The proposed AdCorDA method is based on modification of the training set and mak…
Robustness to distribution shifts of compressed networks for edge devices
Lulan Shen, Ali Edalati, Brett Meyer +2
It is necessary to develop efficient DNNs deployed on edge devices with limited computation resources. However, the compressed networks often execute new tasks in the target domain…
Step-GRAND: A Low Latency Universal Soft-input Decoder
Syed Mohsin Abbas, Marwan Jalaleddine, Chi-Ying Tsui +1
GRAND features both soft-input and hard-input variants that are well suited to efficient hardware implementations that can be characterized with achievable average and worst-case d…
SSS3D: Fast Neural Architecture Search For Efficient Three-Dimensional Semantic Segmentation
Olivier Therrien, Marihan Amein, Zhuoran Xiong +2
We present SSS3D, a fast multi-objective NAS framework designed to find computationally efficient 3D semantic scene segmentation networks. It uses RandLA-Net, an off-the-shelf poin…