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.

Recruiting

  • 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!

Topics

Acceleration ADMM Adversarial Robustness 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 Metric learning Mismatch Removal Neural Network 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 Structure from Motion Subspace clustering Subspace learning Subspace Recovery Super-Resolution Superresolution t-SVD

Recentest Publications

Quickly discover relevant content by filtering publications.

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 …

Invertible Image Rescaling. ECCV, 2020.

High-resolution digital images are usually downscaled to fit various display screens or save the cost of storage and bandwidth, …

Accelerated First-Order Optimization Algorithms for Machine Learning. P IEEE, 2020.

Numerical optimization serves as one of the pillars of machine learning. To meet the demands of big data applications, lots of efforts …

Boosted Histogram Transform for Regression. ICML, 2020.

We propose a boosting algorithm for regression problems called boosted histogram transform for regression (BHTR) based on histogram …

Implicit Euler Skip Connections: Enhancing Adversarial Robustness via Numerical Stability. ICML, 2020.

We explore the relationship between the adversarial robustness and numerical stability. Furthermore, we propose IE-Skips, which is a …

Maximum-and-Concatenation Networks. ICML, 2020.

We propose a novel multi-layer DNN structure termed MCN, which can approximate some class of continuous functions arbitrarily well even …

PDO-eConvs: Partial Differential Operator Based Equivariant Convolutions. ICML, 2020.

We employ PDOs as steerable filters to design a system equivariant to E(n). We use numerical schemes to discretize this system and …

On the Complexity Analysis of the Primal Solutions for the Accelerated Randomized Dual Coordinate Ascent. JMLR, 2020.

Our study aims to give the convergence rate analysis of the primal solutions for the accelerated randomized dual coordinate ascent.

Contact

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