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20152024
most citedFast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video

71 citations · 209 across the 28 of their papers we have counts for

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Showing 2017Show all

6 papers · 1 filter

cs.NE201724 cited

SquishedNets: Squishing SqueezeNet further for edge device scenarios via deep evolutionary synthesis

Mohammad Javad Shafiee, Francis Li, Brendan Chwyl +1

While deep neural networks have been shown in recent years to outperform other machine learning methods in a wide range of applications, one of the biggest challenges with enabling…

cs.CV2017

Discovery Radiomics via Deep Multi-Column Radiomic Sequencers for Skin Cancer Detection

Mohammad Javad Shafiee, Alexander Wong

While skin cancer is the most diagnosed form of cancer in men and women, with more cases diagnosed each year than all other cancers combined, sufficiently early diagnosis results i…

cs.CV201771 cited

Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video

Mohammad Javad Shafiee, Brendan Chywl, Francis Li +1

Object detection is considered one of the most challenging problems in this field of computer vision, as it involves the combination of object classification and object localizatio…

cs.NE2017

The Mating Rituals of Deep Neural Networks: Learning Compact Feature Representations through Sexual Evolutionary Synthesis

Audrey Chung, Mohammad Javad Shafiee, Paul Fieguth +1

Evolutionary deep intelligence was recently proposed as a method for achieving highly efficient deep neural network architectures over successive generations. Drawing inspiration f…

cs.NE20173 cited

Exploring the Imposition of Synaptic Precision Restrictions For Evolutionary Synthesis of Deep Neural Networks

Mohammad Javad Shafiee, Francis Li, Alexander Wong

A key contributing factor to incredible success of deep neural networks has been the significant rise on massively parallel computing devices allowing researchers to greatly increa…

cs.NE20172 cited

Synthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions

A. H. Karimi, M. J. Shafiee, A. Ghodsi +1

In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distri…