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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...
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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...
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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...
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05/25/2022
MAPLE-X: Latency Prediction with Explicit Microprocessor Prior Knowledge
Deep neural network (DNN) latency characterization is a time-consuming p...
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04/27/2022
MAPLE-Edge: A Runtime Latency Predictor for Edge Devices
Neural Architecture Search (NAS) has enabled automatic discovery of more...
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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...
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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...
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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...
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11/30/2021
MAPLE: Microprocessor A Priori for Latency Estimation
Modern deep neural networks must demonstrate state-of-the-art accuracy w...
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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...
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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...
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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 ...
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06/18/2021
Residual Error: a New Performance Measure for Adversarial Robustness
Despite the significant advances in deep learning over the past decade, ...
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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...
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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...
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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...
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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...
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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...
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08/01/2020
Vulnerability Under Adversarial Machine Learning: Bias or Variance?
Prior studies have unveiled the vulnerability of the deep neural network...
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03/04/2020
Deep Neural Network Perception Models and Robust Autonomous Driving Systems
This paper analyzes the robustness of deep learning models in autonomous...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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...
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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 ...
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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 ...
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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...
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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...
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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...
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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,...
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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...
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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...
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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 ...
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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...
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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...
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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...
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06/14/2016
Deep Learning with Darwin: Evolutionary Synthesis of Deep Neural Networks
Taking inspiration from biological evolution, we explore the idea of "Ca...
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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...
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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...
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12/18/2015
Domain Adaptation and Transfer Learning in StochasticNets
Transfer learning is a recent field of machine learning research that ai...
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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...
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12/11/2015
Efficient Deep Feature Learning and Extraction via StochasticNets
Deep neural networks are a powerful tool for feature learning and extrac...
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11/11/2015
Discovery Radiomics via StochasticNet Sequencers for Cancer Detection
Radiomics has proven to be a powerful prognostic tool for cancer detecti...
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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...
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09/01/2015