
ATGAN: A Generative Attack Model for Adversarial Transferring on Generative Adversarial Nets
Recent studies have discovered the vulnerability of Deep Neural Networks...
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Adaptive Large Neighborhood Search for Circle Bin Packing Problem
We address a new variant of packing problem called the circle bin packin...
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Robust Local Features for Improving the Generalization of Adversarial Training
Adversarial training has been demonstrated as one of the most effective ...
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Single Image Reflection Removal through Cascaded Refinement
We address the problem of removing undesirable reflections from a single...
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Child Gender Determination with Convolutional Neural Networks on Hand RadioGraphs
Motivation: In forensic or medicolegal investigation as well as in anth...
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Effective reinforcement learning based local search for the maximum kplex problem
The maximum kplex problem is a computationally complex problem, which e...
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A New Anchor Word Selection Method for the Separable Topic Discovery
Separable Nonnegative Matrix Factorization (SNMF) is an important metho...
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Hashing with Binary Matrix Pursuit
We propose theoretical and empirical improvements for twostage hashing ...
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Nesterov Accelerated Gradient and Scale Invariance for Improving Transferability of Adversarial Examples
Recent evidence suggests that deep neural networks (DNNs) are vulnerable...
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The Local Dimension of Deep Manifold
Based on our observation that there exists a dramatic drop for the singu...
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Hidden Community Detection in Social Networks
We introduce a new paradigm that is important for community detection in...
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Hashing as TieAware Learning to Rank
Hashing, or learning binary embeddings of data, is frequently used in ne...
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Understanding Deep Representations through Random Weights
We systematically study the deep representation of random weight CNN (co...
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MIHash: Online Hashing with Mutual Information
Learningbased hashing methods are widely used for nearest neighbor retr...
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A Powerful Generative Model Using Random Weights for the Deep Image Representation
To what extent is the success of deep visualization due to the training?...
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Generalized MajorizationMinimization
Nonconvex optimization is ubiquitous in machine learning. The Majorizat...
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Variable Version Lovász Local Lemma: Beyond Shearer's Bound
A tight criterion under which the abstract version Lovász Local Lemma (a...
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Hashing with Mutual Information
Binary vector embeddings enable fast nearest neighbor retrieval in large...
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Local Descriptors Optimized for Average Precision
Extraction of local feature descriptors is a vital stage in the solution...
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TexttoClip Video Retrieval with Early Fusion and ReCaptioning
We propose a novel method capable of retrieving clips from untrimmed vid...
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Rectangle Transformation Problem
In this paper, we propose the rectangle transformation problem (RTP) and...
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A direct approach to false discovery rates by decoy permutations
The current approaches to false discovery rates (FDRs) in multiple hypot...
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An Iterative PathBreaking Approach with Mutation and Restart Strategies for the MAXSAT Problem
Although PathRelinking is an effective local search method for many com...
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Quantum Lovász Local Lemma: Shearer's Bound is Tight
Lovász Local Lemma (LLL) is a very powerful tool in combinatorics and pr...
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Improving the Generalization of Adversarial Training with Domain Adaptation
By injecting adversarial examples into training data, the adversarial tr...
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Towards Understanding Learning Representations: To What Extent Do Different Neural Networks Learn the Same Representation
It is widely believed that learning good representations is one of the m...
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Tight bounds for popping algorithms
We sharpen runtime analysis for algorithms under the partial rejection ...
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Adaptive Wavelet Clustering for High Noise Data
In this paper we make progress on the unsupervised task of mining arbitr...
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Adaptive Wavelet Clustering for Highly Noisy Data
In this paper we make progress on the unsupervised task of mining arbitr...
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A Learning based Branch and Bound for Maximum Common Subgraph Problems
Branchandbound (BnB) algorithms are widely used to solve combinatorial...
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Adversarially Robust Generalization Just Requires More Unlabeled Data
Neural network robustness has recently been highlighted by the existence...
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Dynamic MCMC Sampling
The Markov chain Monte Carlo (MCMC) methods are the primary tools for sa...
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Natural Language Adversarial Attacks and Defenses in Word Level
Up until recent two years, inspired by the big amount of research about ...
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Errorfeedback Stochastic Configuration Strategy on Convolutional Neural Networks for Time Series Forecasting
Despite the superiority of convolutional neural networks demonstrated in...
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Stochastic Item Descent Method for Large Scale Equal Circle Packing Problem
Stochastic gradient descent (SGD) is a powerful method for largescale o...
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Kun He
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Professor in School of Computer Science and Technology at Huazhong University of Science and Technology