68 citations · 493 across the 40 of their papers we have counts for
9 papers · 1 filter
Emergence of hierarchical modes from deep learning
Chan Li, Haiping Huang
Large-scale deep neural networks consume expensive training costs, but the training results in less-interpretable weight matrices constructing the networks. Here, we propose a mode…
Data-driven effective model shows a liquid-like deep learning
Wenxuan Zou, Haiping Huang
The geometric structure of an optimization landscape is argued to be fundamentally important to support the success of deep neural network learning. A direct computation of the lan…
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…
Mechanisms of dimensionality reduction and decorrelation in deep neural networks
Haiping Huang
Deep neural networks are widely used in various domains. However, the nature of computations at each layer of the deep networks is far from being well understood. Increasing the in…
Random active path model of deep neural networks with diluted binary synapses
Haiping Huang, Alireza Goudarzi
Deep learning has become a powerful and popular tool for a variety of machine learning tasks. However, it is challenging to understand the mechanism of deep learning from a theoret…
Reinforced stochastic gradient descent for deep neural network learning
Haiping Huang, Taro Toyoizumi
Stochastic gradient descent (SGD) is a standard optimization method to minimize a training error with respect to network parameters in modern neural network learning. However, it t…
Statistical mechanics of unsupervised feature learning in a restricted Boltzmann machine with binary synapses
Haiping Huang
Revealing hidden features in unlabeled data is called unsupervised feature learning, which plays an important role in pretraining a deep neural network. Here we provide a statistic…