11 citations · 36 across the 17 of their papers we have counts for
17 papers
GHN-QAT: Training Graph Hypernetworks to Predict Quantization-Robust Parameters of Unseen Limited Precision Neural Networks
Stone Yun, Alexander Wong
Graph Hypernetworks (GHN) can predict the parameters of varying unseen CNN architectures with surprisingly good accuracy at a fraction of the cost of iterative optimization. Follow…
Explaining Explainability: Towards Deeper Actionable Insights into Deep Learning through Second-order Explainability
E. Zhixuan Zeng, Hayden Gunraj, Sheldon Fernandez +1
Explainability plays a crucial role in providing a more comprehensive understanding of deep learning models' behaviour. This allows for thorough validation of the model's performan…
DeepfakeArt Challenge: A Benchmark Dataset for Generative AI Art Forgery and Data Poisoning Detection
Hossein Aboutalebi, Dayou Mao, Rongqi Fan +3
The tremendous recent advances in generative artificial intelligence techniques have led to significant successes and promise in a wide range of different applications ranging from…
Plankton-FL: Exploration of Federated Learning for Privacy-Preserving Training of Deep Neural Networks for Phytoplankton Classification
Daniel Zhang, Vikram Voleti, Alexander Wong +1
Creating high-performance generalizable deep neural networks for phytoplankton monitoring requires utilizing large-scale data coming from diverse global water sources. A major chal…
A Fair Loss Function for Network Pruning
Robbie Meyer, Alexander Wong
Model pruning can enable the deployment of neural networks in environments with resource constraints. While pruning may have a small effect on the overall performance of the model,…
GHN-Q: Parameter Prediction for Unseen Quantized Convolutional Architectures via Graph Hypernetworks
Stone Yun, Alexander Wong
Deep convolutional neural network (CNN) training via iterative optimization has had incredible success in finding optimal parameters. However, modern CNN architectures often contai…
TinyDefectNet: Highly Compact Deep Neural Network Architecture for High-Throughput Manufacturing Visual Quality Inspection
Mohammad Javad Shafiee, Mahmoud Famouri, Gautam Bathla +2
A critical aspect in the manufacturing process is the visual quality inspection of manufactured components for defects and flaws. Human-only visual inspection can be very time-cons…
Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning
Maziar Gomrokchi, Susan Amin, Hossein Aboutalebi +2
While significant research advances have been made in the field of deep reinforcement learning, there have been no concrete adversarial attack strategies in literature tailored for…
Does Form Follow Function? An Empirical Exploration of the Impact of Deep Neural Network Architecture Design on Hardware-Specific Acceleration
Saad Abbasi, Mohammad Javad Shafiee, Ellick Chan +1
The fine-grained relationship between form and function with respect to deep neural network architecture design and hardware-specific acceleration is one area that is not well stud…
Residual Error: a New Performance Measure for Adversarial Robustness
Hossein Aboutalebi, Mohammad Javad Shafiee, Michelle Karg +2
Despite the significant advances in deep learning over the past decade, a major challenge that limits the wide-spread adoption of deep learning has been their fragility to adversar…
AttendSeg: A Tiny Attention Condenser Neural Network for Semantic Segmentation on the Edge
Xiaoyu Wen, Mahmoud Famouri, Andrew Hryniowski +1
In this study, we introduce \textbf{AttendSeg}, a low-precision, highly compact deep neural network tailored for on-device semantic segmentation. AttendSeg possesses a self-attenti…
Do All MobileNets Quantize Poorly? Gaining Insights into the Effect of Quantization on Depthwise Separable Convolutional Networks Through the Eyes of Multi-scale Distributional Dynamics
Stone Yun, Alexander Wong
As the "Mobile AI" revolution continues to grow, so does the need to understand the behaviour of edge-deployed deep neural networks. In particular, MobileNets are the go-to family…
Beyond Explainability: Leveraging Interpretability for Improved Adversarial Learning
Devinder Kumar, Ibrahim Ben-Daya, Kanav Vats +3
In this study, we propose the leveraging of interpretability for tasks beyond purely the purpose of explainability. In particular, this study puts forward a novel strategy for leve…
SISC: End-to-end Interpretable Discovery Radiomics-Driven Lung Cancer Prediction via Stacked Interpretable Sequencing Cells
Vignesh Sankar, Devinder Kumar, David A. Clausi +2
Objective: Lung cancer is the leading cause of cancer-related death worldwide. Computer-aided diagnosis (CAD) systems have shown significant promise in recent years for facilitatin…
Discovery Radiomics with CLEAR-DR: Interpretable Computer Aided Diagnosis of Diabetic Retinopathy
Devinder Kumar, Graham W. Taylor, Alexander Wong
Objective: Radiomics-driven Computer Aided Diagnosis (CAD) has shown considerable promise in recent years as a potential tool for improving clinical decision support in medical onc…
Opening the Black Box of Financial AI with CLEAR-Trade: A CLass-Enhanced Attentive Response Approach for Explaining and Visualizing Deep Learning-Driven Stock Market Prediction
Devinder Kumar, Graham W Taylor, Alexander Wong
Deep learning has been shown to outperform traditional machine learning algorithms across a wide range of problem domains. However, current deep learning algorithms have been criti…
Explaining the Unexplained: A CLass-Enhanced Attentive Response (CLEAR) Approach to Understanding Deep Neural Networks
Devinder Kumar, Alexander Wong, Graham W. Taylor
In this work, we propose CLass-Enhanced Attentive Response (CLEAR): an approach to visualize and understand the decisions made by deep neural networks (DNNs) given a specific input…