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.


  • Our group at Peking University is recruiting tenure-track faculties and PostDocs (academic or industrial). For the postdoc program in 2020, please refer to the Chinese version or the English version.
  • Our group at Peking University is recruiting Ph.D.s who have strong mathematical abilities (however, this does not imply that you have to come from mathematics department) and great interest in theoretical analysis in order to enjoy with me how to use mathematics to solve real problems elegantly.
  • Our group at Peking University is also recruiting Masters who have strong coding skills and interests in Quantitative Trading (a reference book). Welcome to send me your detailed resume!
  • Our group at Zhijiang LAB is recruiting Researchers and PostDocs.
  • Samsung AI Lab is recruiting top-caliber researchers with strong expertise in machine learning!


Acceleration ADMM Adversarial Robustness Adversarial Transferability Alternating direction method Alternating Direction Method of Multipliers Bayes error Boosting Color Filter Array Compressed Sensing Compressive Phase Retrieval, computer vision Contextual distance Convergence Analysis Convex Optimization Data Compression Deep Learning Demosaicking Denoising Dictionary Learning Dimensionality reduction Discriminant analysis Document analysis Double quantization Expectation Maximization Face recognition Feature detection Feature extraction forgery Geometric Optimization Handwriting recognition Image Annotation Image classification Image Denoising Image Processing Image Reconstruction Image rectification Image restoration Image Retrieval Image segmentation Laplace equations Learning-based PDEs Light field Linear discriminant analysis Lorentzian geometry Low Rank Low Rank Representation Low-level vision Lumigraph Machine Learning Majorization Minimization Manifold learning Manifolds Matrix Completion matrix decomposition Metric learning Mismatch Removal Neural Network Neural networks Nonconvex Optimization Optimal control Optimization Partial Differential Equation plenoptic functions Pose Estimation Principal component analysis Robust PCA Robust Principal Component Analysis sampling Semantic segmentation Semi-supervised learning Singular value decomposition Sparse Coding Sparse matrices Sparse Representation Spectral clustering Subspace clustering Subspace Recovery Super-Resolution Superresolution

Recentest Publications

AdaGCN: Adaboosting Graph Convolutional Networks into Deep Models. ICLR, 2021.

The design of deep graph models still remains to be investigated and the crucial part is how to explore and exploit the knowledge from …

Is Attention Better Than Matrix Decomposition?. ICLR, 2021.

As an essential ingredient of modern deep learning, attention mechanism, especially self-attention, plays a vital role in the global …

Learned Extragradient ISTA with Interpretable Residual Structures for Sparse Coding. AAAI, 2021.

Recently, the study on learned iterative shrinkage thresholding algorithm (LISTA) has attracted increasing attentions. A large number …

PDO-eS2CNNs: Partial Differential Operator Based Equivariant Spherical CNNs. AAAI, 2021.

Spherical signals exist in many applications, e.g., planetary data, LiDAR scans and digitalization of 3D objects, calling for models …

Training Neural Networks by Lifted Proximal Operator Machines. TPAMI, 2020.

We present the lifted proximal operator machine (LPOM) to train fully-connected feed-forward neural networks. LPOM represents the …

ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding. NeurIPS, 2020.

Neural architecture search (NAS) aims to produce the optimal sparse solution from a high-dimensional space spanned by all candidate …

Decentralized Accelerated Gradient Methods With Increasing Penalty Parameters. IEEE T. Signal Processing, 2020.

In this paper, we study the communication and (sub)gradient computation costs in distributed optimization and give a sharp complexity …

Improving Semantic Segmentation via Decoupled Body and Edge Supervision. ECCV, 2020.

Existing semantic segmentation approaches either aim to improve the object’s inner consistency by modeling the global context, or …


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