3 citations · 7 across the 7 of their papers we have counts for
7 papers
Geometric origin of adversarial vulnerability in deep learning
Yixiong Ren, Wenkang Du, Jianhui Zhou +1
Balancing training accuracy and adversarial robustness has beeen a challenge since the birth of deep learning. Here, we introduce a geometry-aware deep learning framework that leve…
Response function as a quantitative measure of consciousness in brain dynamics
Wenkang Du, Haiping Huang
Understanding the neural correlates of consciousness remains a central challenge in neuroscience. In this study, we investigate the relationship between consciousness and neural re…
Kinetic energy in random recurrent neural networks
Li-Ru Zhang, Haiping Huang
High-dimensional chaotic dynamics can emerge in a large random recurrent neural network when the synaptic gain crosses a threshold. Recent works showed that the kinetic energy of n…
Freezing chaos without synaptic plasticity
Weizhong Huang, Haiping Huang
Chaos is ubiquitous in high-dimensional neural dynamics. A strong chaotic fluctuation may be harmful to information processing. A traditional way to mitigate this issue is to intro…
Synaptic plasticity alters the nature of chaos transition in neural networks
Wenkang Du, Haiping Huang
In realistic neural circuits, both neurons and synapses are coupled in dynamics with separate time scales. The circuit functions are intimately related to these coupled dynamics. H…
How high dimensional neural dynamics are confined in phase space
Shishe Wang, Haiping Huang
High dimensional dynamics play a vital role in brain function, ecological systems, and neuro-inspired machine learning. Where and how these dynamics are confined in the phase space…
Fermi-Bose Machine achieves both generalization and adversarial robustness
Mingshan Xie, Yuchen Wang, Haiping Huang
Distinct from human cognitive processing, deep neural networks trained by backpropagation can be easily fooled by adversarial examples. To design a semantically meaningful represen…