Zlin’s Extraordinary Research Oasis

Zeal, Excellence, Reliability and Openness

Welcome to the ZERO Lab, the research group lead by Prof. Zhouchen Lin (Zlin), affiliated to School of Electronics Engineering and Computer Science, Peking University. We research on machine learning and computer vision.

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Recentest Publications

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Unified Graph and Low-rank Tensor Learning for Multi-view Clustering. AAAI, 2020.

We propose a novel framework to jointly learn the affinity graph and low-rank tensor decomposition for multi-view clustering.

Dynamical System Inspired Adaptive Time Stepping Controller for Residual Network Families. AAAI, 2020.

Inspired from the dynamical systems, this study aims to unravel and improve ResNets.

Multi-Stage Self-Supervised Learning for Graph Convolutional Networks on Graphs with Few Labeled Nodes. AAAI, 2020.

We propose a novel training algorithm to improve the generalization performance of GCNs on graphs with few labeled nodes.

SOGNet: Scene Overlap Graph Network for Panoptic Segmentation. AAAI, 2020.

Our study aims to explicitly predict overlap relations and resolve overlaps in a differentiable way for the panoptic output.

Accelerated Alternating Direction Method of Multipliers:An Optimal O(1/K) Nonergodic Analysis. JSC, 2019.

The Alternating Direction Method of Multipliers (ADMM) is widely used for linearly constrained convex problems. It is proven to have an …

L1-Norm Heteroscedastic Discriminant Analysis under Mixture of Gaussian Distributions . TNNLS, 2019.

Fisher’s criterion is one of the most popular discriminant criteria for feature extraction. It is defined as the generalized Rayleigh …

On the Convergence of Learning-based Iterative Methods for Nonconvex Inverse Problems. TPAMI, 2019.

Numerous tasks at the core of statistics, learning and vision areas are specific cases of ill-posed inverse problems. Recently, …

Expectation Maximization Attention Networks for Semantic Segmentation. ICCV, 2019.

We formulate the attention mechanism into an expectation-maximization manner and iteratively estimate a much more compact set of bases …

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