Search Results for author: Qingyun Sun

Found 16 papers, 8 papers with code

Curvature Graph Generative Adversarial Networks

1 code implementation3 Mar 2022 JianXin Li, Xingcheng Fu, Qingyun Sun, Cheng Ji, Jiajun Tan, Jia Wu, Hao Peng

In this paper, we proposed a novel Curvature Graph Generative Adversarial Networks method, named \textbf{\modelname}, which is the first GAN-based graph representation method in the Riemannian geometric manifold.

Graph Structure Learning with Variational Information Bottleneck

1 code implementation16 Dec 2021 Qingyun Sun, JianXin Li, Hao Peng, Jia Wu, Xingcheng Fu, Cheng Ji, Philip S. Yu

Graph Neural Networks (GNNs) have shown promising results on a broad spectrum of applications.

Graph structure learning

ACE-HGNN: Adaptive Curvature Exploration Hyperbolic Graph Neural Network

1 code implementation15 Oct 2021 Xingcheng Fu, JianXin Li, Jia Wu, Qingyun Sun, Cheng Ji, Senzhang Wang, Jiajun Tan, Hao Peng, Philip S. Yu

Hyperbolic Graph Neural Networks(HGNNs) extend GNNs to hyperbolic space and thus are more effective to capture the hierarchical structures of graphs in node representation learning.

Graph Learning Multi-agent Reinforcement Learning +1

Convex Sparse Blind Deconvolution

no code implementations13 Jun 2021 Qingyun Sun, David Donoho

To bridge the gulf between reported successes and theory's limited understanding, we exhibit a convex optimization problem that -- assuming signal sparsity -- can convert a crude approximation to the true filter into a high-accuracy recovery of the true filter.

A Robust and Generalized Framework for Adversarial Graph Embedding

1 code implementation22 May 2021 JianXin Li, Xingcheng Fu, Hao Peng, Senzhang Wang, Shijie Zhu, Qingyun Sun, Philip S. Yu, Lifang He

With the prevalence of graph data in real-world applications, many methods have been proposed in recent years to learn high-quality graph embedding vectors various types of graphs.

Graph Embedding Graph Mining +3

A Recipe for Global Convergence Guarantee in Deep Neural Networks

no code implementations12 Apr 2021 Kenji Kawaguchi, Qingyun Sun

Existing global convergence guarantees of (stochastic) gradient descent do not apply to practical deep networks in the practical regime of deep learning beyond the neural tangent kernel (NTK) regime.

GRAC: Self-Guided and Self-Regularized Actor-Critic

1 code implementation18 Sep 2020 Lin Shao, Yifan You, Mengyuan Yan, Qingyun Sun, Jeannette Bohg

One dominant component of recent deep reinforcement learning algorithms is the target network which mitigates the divergence when learning the Q function.

Decision Making OpenAI Gym +1

Pairwise Learning for Name Disambiguation in Large-Scale Heterogeneous Academic Networks

no code implementations30 Aug 2020 Qingyun Sun, Hao Peng, Jian-Xin Li, Senzhang Wang, Xiangyu Dong, Liangxuan Zhao, Philip S. Yu, Lifang He

Although these attributes may change, an author's co-authors and research topics do not change frequently with time, which means that papers within a period have similar text and relation information in the academic network.

Graph Embedding

How to Close Sim-Real Gap? Transfer with Segmentation!

no code implementations14 May 2020 Mengyuan Yan, Qingyun Sun, Iuri Frosio, Stephen Tyree, Jan Kautz

Combining the control policy learned from simulation with the perception model, we achieve an impressive $\bf{88\%}$ success rate in grasping a tiny sphere with a real robot.


Stochastic Modified Equations for Continuous Limit of Stochastic ADMM

no code implementations7 Mar 2020 Xiang Zhou, Huizhuo Yuan, Chris Junchi Li, Qingyun Sun

In this work, we put different variants of stochastic ADMM into a unified form, which includes standard, linearized and gradient-based ADMM with relaxation, and study their dynamics via a continuous-time model approach.

Degrees of Freedom Analysis of Unrolled Neural Networks

no code implementations10 Jun 2019 Morteza Mardani, Qingyun Sun, Vardan Papyan, Shreyas Vasanawala, John Pauly, David Donoho

Leveraging the Stein's Unbiased Risk Estimator (SURE), this paper analyzes the generalization risk with its bias and variance components for recurrent unrolled networks.

Image Restoration

A PID Controller Approach for Stochastic Optimization of Deep Networks

3 code implementations CVPR 2018 Wangpeng An, Haoqian Wang, Qingyun Sun, Jun Xu, Qionghai Dai, Lei Zhang

We first reveal the intrinsic connections between SGD-Momentum and PID based controller, then present the optimization algorithm which exploits the past, current, and change of gradients to update the network parameters.

Stochastic Optimization

Neural Proximal Gradient Descent for Compressive Imaging

1 code implementation NeurIPS 2018 Morteza Mardani, Qingyun Sun, Shreyas Vasawanala, Vardan Papyan, Hatef Monajemi, John Pauly, David Donoho

Recovering high-resolution images from limited sensory data typically leads to a serious ill-posed inverse problem, demanding inversion algorithms that effectively capture the prior information.

Bayesian Opponent Exploitation in Imperfect-Information Games

no code implementations10 Mar 2016 Sam Ganzfried, Qingyun Sun

The natural setting for opponent exploitation is the Bayesian setting where we have a prior model that is integrated with observations to create a posterior opponent model that we respond to.

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