1 code implementation • 3 Jun 2019 • Runtian Zhai, Tianle Cai, Di He, Chen Dan, Kun He, John Hopcroft, Li-Wei Wang
Neural network robustness has recently been highlighted by the existence of adversarial examples.
1 code implementation • NeurIPS 2018 • Liwei Wang, Lunjia Hu, Jiayuan Gu, Yue Wu, Zhiqiang Hu, Kun He, John Hopcroft
The theory gives a complete characterization of the structure of neuron activation subspace matches, where the core concepts are maximum match and simple match which describe the overall and the finest similarity between sets of neurons in two networks respectively.
2 code implementations • CVPR 2017 • Xun Huang, Yixuan Li, Omid Poursaeed, John Hopcroft, Serge Belongie
In this paper, we propose a novel generative model named Stacked Generative Adversarial Networks (SGAN), which is trained to invert the hierarchical representations of a bottom-up discriminative network.
Ranked #11 on Conditional Image Generation on CIFAR-10 (Inception score metric)
1 code implementation • NeurIPS 2016 • Kun He, Yan Wang, John Hopcroft
To our knowledge this is the first demonstration of image representations using untrained deep neural networks.
1 code implementation • 24 Nov 2015 • Yixuan Li, Jason Yosinski, Jeff Clune, Hod Lipson, John Hopcroft
Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers.
1 code implementation • 25 Sep 2015 • Yixuan Li, Kun He, David Bindel, John Hopcroft
Nowadays, as we often explore networks with billions of vertices and find communities of size hundreds, it is crucial to shift our attention from macroscopic structure to microscopic structure when dealing with large networks.
Social and Information Networks Data Structures and Algorithms Physics and Society G.2.2; H.3.3
2 code implementations • 23 Jan 2015 • Kun He, Sucheta Soundarajan, Xuezhi Cao, John Hopcroft, Menglong Huang
Additionally, on both real and synthetic networks containing a hidden ground-truth community structure, HICODE uncovers this structure better than any baseline algorithms that we compared against.
Social and Information Networks Physics and Society
no code implementations • NeurIPS 2013 • Ping Li, Gennady Samorodnitsk, John Hopcroft
The method of Cauchy random projections is popular for computing the $l_1$ distance in high dimension.
no code implementations • 5 Aug 2013 • Ping Li, Gennady Samorodnitsky, John Hopcroft
The method of stable random projections is popular for efficiently computing the Lp distances in high dimension (where 0<p<=2), using small space.