Mohammad Javad Shafiee

research

∙ 04/21/2023

Fast GraspNeXt: A Fast Self-Attention Neural Network Architecture for Multi-task Learning in Computer Vision Tasks for Robotic Grasping on the Edge

Multi-task learning has shown considerable promise for improving the per...

0 Alexander Wong, et al. ∙

research

∙ 12/20/2022

High-Throughput, High-Performance Deep Learning-Driven Light Guide Plate Surface Visual Quality Inspection Tailored for Real-World Manufacturing Environments

Light guide plates are essential optical components widely used in a div...

0 Carol Xu, et al. ∙

research

∙ 08/15/2022

Faster Attention Is What You Need: A Fast Self-Attention Neural Network Backbone Architecture for the Edge via Double-Condensing Attention Condensers

With the growing adoption of deep learning for on-device TinyML applicat...

0 Alexander Wong, et al. ∙

research

∙ 05/25/2022

MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge

Deep neural network (DNN) latency characterization is a time-consuming p...

0 Saad Abbasi, et al. ∙

research

∙ 04/27/2022

MAPLE-Edge: A Runtime Latency Predictor for Edge Devices

Neural Architecture Search (NAS) has enabled automatic discovery of more...

0 Saeejith Nair, et al. ∙

research

∙ 04/25/2022

CellDefectNet: A Machine-designed Attention Condenser Network for Electroluminescence-based Photovoltaic Cell Defect Inspection

Photovoltaic cells are electronic devices that convert light energy to e...

0 Carol Xu, et al. ∙

research

∙ 04/25/2022

LightDefectNet: A Highly Compact Deep Anti-Aliased Attention Condenser Neural Network Architecture for Light Guide Plate Surface Defect Detection

Light guide plates are essential optical components widely used in a div...

0 Carol Xu, et al. ∙

research

∙ 04/24/2022

COVID-Net Biochem: An Explainability-driven Framework to Building Machine Learning Models for Predicting Survival and Kidney Injury of COVID-19 Patients from Clinical and Bioch

Ever since the declaration of COVID-19 as a pandemic by the World Health...

0 Hossein Aboutalebi, et al. ∙

research

∙ 11/30/2021

MAPLE: Microprocessor A Priori for Latency Estimation

Modern deep neural networks must demonstrate state-of-the-art accuracy w...

0 Saad Abbasi, et al. ∙

research

∙ 11/29/2021

TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection

A critical aspect in the manufacturing process is the visual quality ins...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 10/12/2021

MEDUSA: Multi-scale Encoder-Decoder Self-Attention Deep Neural Network Architecture for Medical Image Analysis

Medical image analysis continues to hold interesting challenges given th...

0 Hossein Aboutalebi, et al. ∙

research

∙ 07/08/2021

Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration

The fine-grained relationship between form and function with respect to ...

0 Saad Abbasi, et al. ∙

research

∙ 06/18/2021

Residual Error: a New Performance Measure for Adversarial Robustness

Despite the significant advances in deep learning over the past decade, ...

0 Hossein Aboutalebi, et al. ∙

research

∙ 05/04/2021

COVID-Net CT-S: 3D Convolutional Neural Network Architectures for COVID-19 Severity Assessment using Chest CT Images

The health and socioeconomic difficulties caused by the COVID-19 pandemi...

0 Hossein Aboutalebi, et al. ∙

research

∙ 05/01/2021

COVID-Net CXR-S: Deep Convolutional Neural Network for Severity Assessment of COVID-19 Cases from Chest X-ray Images

The world is still struggling in controlling and containing the spread o...

15 Hossein Aboutalebi, et al. ∙

research

∙ 03/31/2021

OutlierNets: Highly Compact Deep Autoencoder Network Architectures for On-Device Acoustic Anomaly Detection

Human operators often diagnose industrial machinery via anomalous sounds...

0 Saad Abbasi, et al. ∙

research

∙ 12/25/2020

A Simple Fine-tuning Is All You Need: Towards Robust Deep Learning Via Adversarial Fine-tuning

Adversarial Training (AT) with Projected Gradient Descent (PGD) is an ef...

0 Ahmadreza Jeddi, et al. ∙

research

∙ 09/30/2020

AttendNets: Tiny Deep Image Recognition Neural Networks for the Edge via Visual Attention Condensers

While significant advances in deep learning has resulted in state-of-the...

0 Alexander Wong, et al. ∙

research

∙ 08/01/2020

Vulnerability Under Adversarial Machine Learning: Bias or Variance?

Prior studies have unveiled the vulnerability of the deep neural network...

55 Hossein Aboutalebi, et al. ∙

research

∙ 03/04/2020

Deep Neural Network Perception Models and Robust Autonomous Driving Systems

This paper analyzes the robustness of deep learning models in autonomous...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 03/02/2020

Learn2Perturb: an End-to-end Feature Perturbation Learning to Improve Adversarial Robustness

While deep neural networks have been achieving state-of-the-art performa...

0 Ahmadreza Jeddi, et al. ∙

research

∙ 10/16/2019

Explaining with Impact: A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

There has been a significant surge of interest recently around the conce...

50 Zhong Qiu Lin, et al. ∙

research

∙ 10/16/2019

Do Explanations Reflect Decisions? A Machine-centric Strategy to Quantify the Performance of Explainability Algorithms

There has been a significant surge of interest recently around the conce...

0 Zhong Qiu Lin, et al. ∙

research

∙ 10/15/2019

State of Compact Architecture Search For Deep Neural Networks

The design of compact deep neural networks is a crucial task to enable w...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 10/03/2019

YOLO Nano: a Highly Compact You Only Look Once Convolutional Neural Network for Object Detection

Object detection remains an active area of research in the field of comp...

0 Alexander Wong, et al. ∙

research

∙ 09/12/2019

Human-Machine Collaborative Design for Accelerated Design of Compact Deep Neural Networks for Autonomous Driving

An effective deep learning development process is critical for widesprea...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 11/05/2018

Efficient Inference on Deep Neural Networks by Dynamic Representations and Decision Gates

The current trade-off between depth and computational cost makes it diff...

0 Mohammad Saeed Shafiee, et al. ∙

research

∙ 09/17/2018

FermiNets: Learning generative machines to generate efficient neural networks via generative synthesis

The tremendous potential exhibited by deep learning is often offset by a...

0 Alexander Wong, et al. ∙

research

∙ 06/08/2018

Unsupervised Feature Learning Toward a Real-time Vehicle Make and Model Recognition

Vehicle Make and Model Recognition (MMR) systems provide a fully automat...

4 Amir Nazemi, et al. ∙

research

∙ 03/28/2018

muNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification

Traffic sign recognition is a very important computer vision task for a ...

0 Alexander Wong, et al. ∙

research

∙ 03/28/2018

μNet: A Highly Compact Deep Convolutional Neural Network Architecture for Real-time Embedded Traffic Sign Classification

Traffic sign recognition is a very important computer vision task for a ...

0 Alexander Wong, et al. ∙

research

∙ 02/19/2018

Tiny SSD: A Tiny Single-shot Detection Deep Convolutional Neural Network for Real-time Embedded Object Detection

Object detection is a major challenge in computer vision, involving both...

0 Alexander Wong, et al. ∙

research

∙ 01/16/2018

StressedNets: Efficient Feature Representations via Stress-induced Evolutionary Synthesis of Deep Neural Networks

The computational complexity of leveraging deep neural networks for extr...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 11/20/2017

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

While deep neural networks have been shown in recent years to outperform...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 09/24/2017

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

While skin cancer is the most diagnosed form of cancer in men and women,...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 09/18/2017

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

Object detection is considered one of the most challenging problems in t...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 09/07/2017

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

Evolutionary deep intelligence was recently proposed as a method for ach...

0 Audrey Chung, et al. ∙

research

∙ 07/01/2017

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

A key contributing factor to incredible success of deep neural networks ...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 05/10/2017

Discovery Radiomics via Evolutionary Deep Radiomic Sequencer Discovery for Pathologically-Proven Lung Cancer Detection

While lung cancer is the second most diagnosed form of cancer in men and...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 04/07/2017

Evolution in Groups: A deeper look at synaptic cluster driven evolution of deep neural networks

A promising paradigm for achieving highly efficient deep neural networks...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 09/06/2016

Evolutionary Synthesis of Deep Neural Networks via Synaptic Cluster-driven Genetic Encoding

There has been significant recent interest towards achieving highly effi...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 06/14/2016

Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks

Taking inspiration from biological evolution, we explore the idea of "Ca...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 02/01/2016

Scene Invariant Crowd Segmentation and Counting Using Scale-Normalized Histogram of Moving Gradients (HoMG)

The problem of automated crowd segmentation and counting has garnered si...

0 Parthipan Siva, et al. ∙

research

∙ 12/25/2015

Sparse Reconstruction of Compressive Sensing MRI using Cross-Domain Stochastically Fully Connected Conditional Random Fields

Magnetic Resonance Imaging (MRI) is a crucial medical imaging technology...

0 Edward Li, et al. ∙

research

∙ 12/18/2015

Domain Adaptation and Transfer Learning in StochasticNets

Transfer learning is a recent field of machine learning research that ai...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 12/15/2015

Noise-Compensated, Bias-Corrected Diffusion Weighted Endorectal Magnetic Resonance Imaging via a Stochastically Fully-Connected Joint Conditional Random Field Model

Diffusion weighted magnetic resonance imaging (DW-MR) is a powerful tool...

0 Ameneh Boroomand, et al. ∙

research

∙ 12/11/2015

Efficient Deep Feature Learning and Extraction via StochasticNets

Deep neural networks are a powerful tool for feature learning and extrac...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 11/11/2015

Discovery Radiomics via StochasticNet Sequencers for Cancer Detection

Radiomics has proven to be a powerful prognostic tool for cancer detecti...

0 Mohammad Javad Shafiee, et al. ∙

research

∙ 09/01/2015

Discovery Radiomics for Pathologically-Proven Computed Tomography Lung Cancer Prediction

Lung cancer is the leading cause for cancer related deaths. As such, the...

0 Devinder Kumar, et al. ∙

research

∙ 09/01/2015

Discovery Radiomics for Multi-Parametric MRI Prostate Cancer Detection

Prostate cancer is the most diagnosed form of cancer in Canadian men, an...

0 Audrey G. Chung, et al. ∙

Mohammad Javad Shafiee

Featured Co-authors