Search Results for author: John Hopcroft

Found 9 papers, 7 papers with code

Adversarially Robust Generalization Just Requires More Unlabeled Data

1 code implementation3 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.

Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation

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.

Stacked Generative Adversarial Networks

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)

Conditional Image Generation

A Powerful Generative Model Using Random Weights for the Deep Image Representation

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.

Convergent Learning: Do different neural networks learn the same representations?

1 code implementation24 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.


Uncovering the Small Community Structure in Large Networks: A Local Spectral Approach

1 code implementation25 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

Revealing Multiple Layers of Hidden Community Structure in Networks

2 code implementations23 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

Sign Cauchy Projections and Chi-Square Kernel

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.

Sign Stable Projections, Sign Cauchy Projections and Chi-Square Kernels

no code implementations5 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.

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