73 citations · 133 across the 13 of their papers we have counts for
13 papers
Efficient CMOS Invertible Logic Using Stochastic Computing
Sean C. Smithson, Naoya Onizawa, Brett H. Meyer +2
Invertible logic can operate in one of two modes: 1) a forward mode, in which inputs are presented and a single, correct output is produced, and 2) a reverse mode, in which the out…
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…
FMAS: Fast Multi-Objective SuperNet Architecture Search for Semantic Segmentation
Zhuoran Xiong, Marihan Amein, Olivier Therrien +2
We present FMAS, a fast multi-objective neural architecture search framework for semantic segmentation. FMAS subsamples the structure and pre-trained parameters of DeepLabV3+, with…
BD-KD: Balancing the Divergences for Online Knowledge Distillation
Ibtihel Amara, Nazanin Sepahvand, Brett H. Meyer +2
We address the challenge of producing trustworthy and accurate compact models for edge devices. While Knowledge Distillation (KD) has improved model compression in terms of achievi…
CES-KD: Curriculum-based Expert Selection for Guided Knowledge Distillation
Ibtihel Amara, Maryam Ziaeefard, Brett H. Meyer +2
Knowledge distillation (KD) is an effective tool for compressing deep classification models for edge devices. However, the performance of KD is affected by the large capacity gap b…
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…