39 citations · 94 across the 10 of their papers we have counts for
14 papers
Fault-Tolerant Deep Learning: A Hierarchical Perspective
Cheng Liu, Zhen Gao, Siting Liu +3
With the rapid advancements of deep learning in the past decade, it can be foreseen that deep learning will be continuously deployed in more and more safety-critical applications s…
CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance
Tianchen Zhao, Niansong Zhang, Xuefei Ning +3
Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on…
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Nianhui Guo, Joseph Bethge, Haojin Yang +4
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often…
Ensemble-in-One: Learning Ensemble within Random Gated Networks for Enhanced Adversarial Robustness
Yi Cai, Xuefei Ning, Huazhong Yang +1
Adversarial attacks have rendered high security risks on modern deep learning systems. Adversarial training can significantly enhance the robustness of neural network models by sup…
Machine Learning for Electronic Design Automation: A Survey
Guyue Huang, Jingbo Hu, Yifan He +13
With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in…
Discovering Robust Convolutional Architecture at Targeted Capacity: A Multi-Shot Approach
Xuefei Ning, Junbo Zhao, Wenshuo Li +4
Convolutional neural networks (CNNs) are vulnerable to adversarial examples, and studies show that increasing the model capacity of an architecture topology (e.g., width expansion)…