35 citations · 42 across the 2 of their papers we have counts for
9 papers
Relationship between manifold smoothness and adversarial vulnerability in deep learning with local errors
Zijian Jiang, Jianwen Zhou, Haiping Huang
Artificial neural networks can achieve impressive performances, and even outperform humans in some specific tasks. Nevertheless, unlike biological brains, the artificial neural net…
Weakly-correlated synapses promote dimension reduction in deep neural networks
Jianwen Zhou, Haiping Huang
By controlling synaptic and neural correlations, deep learning has achieved empirical successes in improving classification performances. How synaptic correlations affect neural co…
Learning credit assignment
Chan Li, Haiping Huang
Deep learning has achieved impressive prediction accuracies in a variety of scientific and industrial domains. However, the nested non-linear feature of deep learning makes the lea…
Variational mean-field theory for training restricted Boltzmann machines with binary synapses
Haiping Huang
Unsupervised learning requiring only raw data is not only a fundamental function of the cerebral cortex, but also a foundation for a next generation of artificial neural networks.…
Statistical physics of unsupervised learning with prior knowledge in neural networks
Tianqi Hou, Haiping Huang
Integrating sensory inputs with prior beliefs from past experiences in unsupervised learning is a common and fundamental characteristic of brain or artificial neural computation. H…
Minimal model of permutation symmetry in unsupervised learning
Tianqi Hou, K. Y. Michael Wong, Haiping Huang
Permutation of any two hidden units yields invariant properties in typical deep generative neural networks. This permutation symmetry plays an important role in understanding the c…