Shen Yan
yanshen6 at msu dot edu
My name is Shen Yan (严珅). I am a final year PhD at the Computer Science Department at Michigan State Univiersity, where I work on representation learning, AutoML and their applications. I am advised by Mi Zhang.
I got my M.S. in Computer Engineering from RWTH Aachen University, where I worked with Hermann Ney on face recognition and Jens-Rainer Ohm on image retrieval. I have a B.S. in Telecommunications engineering from Xidian University.
GitHub /
Google Scholar /
Twitter /
LinkedIn /
CV
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Research
My general interests lie in machine learning and computer vision. Currently, I focus on representation learning and architecture search, mostly in the context of visual recognition. Representative papers are highlighted.
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Multiview Transformers for Video Recognition
Shen Yan,
Xuehan Xiong,
Anurag Arnab,
Zhichao Lu,
Mi Zhang,
Chen Sun,
Cordelia Schmid
CVPR, 2022  
arXiv /
code /
leaderboard /
bibtex
A simple method for capturing multiresolution temporal context in transformers. State-of-the-art results on popular video classification datasets.
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Deep AutoAugment
Yu Zheng,
Zhi Zhang,
Shen Yan,
Mi Zhang
ICLR, 2022  
arXiv /
code /
bibtex /
slides
Build deep data augmentation policies progressively based on regularized gradient matching.
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NAS-Bench-x11 and the Power of Learning Curves
Shen Yan*,
Colin White*,
Yash Savani,
Frank Hutter
NeurIPS, 2021  
arXiv /
code /
bibtex /
slides
A surrogate method to create multi-fidelity NAS benchmarks.
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CATE: Computation-aware Neural Architecture Encoding with Transformers
Shen Yan,
Kaiqiang Song,
Fei Liu,
Mi Zhang
ICML, 2021 (Long Presentation)
video: 17 min/
arXiv /
code /
bibtex /
poster
Pre-training computation-aware architecture embeddings help architecture search too.
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Does Unsupervised Architecture Representation Learning Help Neural Architecture Search?
Shen Yan,
Yu Zheng,
Wei Ao,
Xiao Zeng,
Mi Zhang
NeurIPS, 2020  
video: 3 min/
arXiv /
code /
bibtex /
poster
Pre-training structure-aware architecture embeddings help architecture search.
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MutualNet: Adaptive ConvNet via Mutual Learning from Network Width and Resolution
Taojiannan Yang,
Sijie Zhu,
Chen Chen,
Shen Yan,
Mi Zhang,
Andrew Wills
ECCV, 2020   (Oral)
video: 10 min/
arXiv /
code /
bibtex
Mutual learning with input resolution and network width improves the accuracy-efficiency tradeoffs.
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Improve Unsupervised Domain Adaptation with Mixup Training
Shen Yan,
Huan Song,
Nanxiang Li,
Lincan Zou,
Liu Ren
arXiv, 2020
arXiv /
code /
bibtex
MixMatch helps unsupervised domain adaptation too.
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HM-NAS: Efficient Neural Architecture Search via Hierarchical Masking
Shen Yan,
Biyi Fang,
Faen Zhang,
Yu Zheng,
Xiao Zeng,
Hui Xu,
Mi Zhang
ICCV Neural Architects Workshop, 2019   (Best Paper Nomination)
arXiv /
bibtex
Highlight the importance of topology learning in differentialable NAS.
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Deep Fisher Faces
Harald Hanselmann,
Shen Yan,
Hermann Ney
BMVC, 2017
bibtex
Extend the center loss with an inter-class loss reminiscent of the popular early face recognition approach Fisherfaces.
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Research Intern, Spring 2021
Abacus.AI, San Francisco, USA
Host: Colin White
Research on multi-fidelity AutoML.
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Applied Machine Learning Intern, Summer 2020
ByteDance Inc., Mountain View, USA
Host: Ming Chen,
Youlong Cheng
Neural architecture search for large scale advertising models.
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Research Intern, Summer 2019
Bosch Research, Sunnyvale, USA
Host: Huan Song,
Liu Ren
Unsupervised domain adaptation with image and time-series data.
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Research Intern, Summer 2017
eBay Research, Aachen, Germany
Host: Shahram Khadivi,
Evgeny Matusov
Adapt neural machine translation to e-commerce domains.
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PC member, AutoML Workshop, ICML 2021
PC member, NAS Workshop, ICLR 2021
Reviewer, NeurIPS 2020, 2021, 2022
Reviewer, ICML 2020, 2021, 2022
Reviewer, ICLR 2021, 2022
Reviewer, CVPR 2021, 2022
Reviewer, ICCV 2021
Reviewer, ECCV 2022
Reviewer, TMLR 2022
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TA for Bachelor, Kinect Programming, Fall 2015
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