9 papers
Derandomizing Dense Binary Hypervector Codebooks for Quantized Scalars
Dmitri Rachkovskij, Evgeny Osipov, Olexander Volkov +2
Hyperdimensional computing and vector symbolic architectures often represent quantized scalar levels by dense binary codebooks whose level-to-level similarity is intended to follow…
Systematic Characterization of Minimal Deep Learning Architectures: A Unified Analysis of Convergence, Pruning, and Quantization
Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4
Deep learning networks excel at classification, yet identifying minimal architectures that reliably solve a task remains challenging. We present a computational methodology for sys…
Deep Learning-Assisted Detection of Sarcopenia in Cross-Sectional Computed Tomography Imaging
Manish Bhardwaj, Huizhi Liang, Ashwin Sivaharan +4
Sarcopenia is a progressive loss of muscle mass and function linked to poor surgical outcomes such as prolonged hospital stays, impaired mobility, and increased mortality. Although…
AdaGAT: Adaptive Guidance Adversarial Training for the Robustness of Deep Neural Networks
Zhenyu Liu, Huizhi Liang, Xinrun Li +2
Adversarial distillation (AD) is a knowledge distillation technique that facilitates the transfer of robustness from teacher deep neural network (DNN) models to lightweight target…
D2R: dual regularization loss with collaborative adversarial generation for model robustness
Zhenyu Liu, Huizhi Liang, Rajiv Ranjan +3
The robustness of Deep Neural Network models is crucial for defending models against adversarial attacks. Recent defense methods have employed collaborative learning frameworks to…
Optimization Strategies for Variational Quantum Algorithms in Noisy Landscapes
Vojtěch Novák, Ivan Zelinka, Václav Snášel
Variational Quantum Algorithms (VQAs) are a leading approach for near-term quantum computing but face major optimization challenges from noise, barren plateaus, and complex energy…
Dynamic Label Adversarial Training for Deep Learning Robustness Against Adversarial Attacks
Zhenyu Liu, Haoran Duan, Huizhi Liang +5
Adversarial training is one of the most effective methods for enhancing model robustness. Recent approaches incorporate adversarial distillation in adversarial training architectur…
On Learnable Parameters of Optimal and Suboptimal Deep Learning Models
Ziwei Zheng, Huizhi Liang, Vaclav Snasel +4
We scrutinize the structural and operational aspects of deep learning models, particularly focusing on the nuances of learnable parameters (weight) statistics, distribution, node i…
Security Assessment of Hierarchical Federated Deep Learning
D Alqattan, R Sun, H Liang +4
Hierarchical federated learning (HFL) is a promising distributed deep learning model training paradigm, but it has crucial security concerns arising from adversarial attacks. This…