6 citations · 11 across the 2 of their papers we have counts for
4 papers
Restructuring, Pruning, and Adjustment of Deep Models for Parallel Distributed Inference
Afshin Abdi, Saeed Rashidi, Faramarz Fekri +1
Using multiple nodes and parallel computing algorithms has become a principal tool to improve training and execution times of deep neural networks as well as effective collective i…
Nested Dithered Quantization for Communication Reduction in Distributed Training
Afshin Abdi, Faramarz Fekri
In distributed training, the communication cost due to the transmission of gradients or the parameters of the deep model is a major bottleneck in scaling up the number of processin…
Compressive Sensing with a Multiple Convex Sets Domain
Hang Zhang, Afshin Abdi, Faramarz Fekri
In this paper, we study a general framework for compressive sensing assuming the existence of the prior knowledge that belongs to the union of multiple convex se…
Fast Convex Pruning of Deep Neural Networks
Alireza Aghasi, Afshin Abdi, Justin Romberg
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network. The method is a convex post-processing module, which prunes (sparsifies) a trained…