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

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.

	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.
	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.

Experience

Research Intern, Summer 2022
Google Research, Brain Team, Mountain View, USA
Host: Jiahui Yu, Tao Zhu, Yuan Cao

Research on large video Transformers.

Research Intern, Student Researcher, Summer 2021, Fall 2021
Google Research, Perception Team, Mountain View, USA
Host: Xuehan Xiong, Zhichao Lu, Chen Sun, Cordelia Schmid

Research on large video Transformers.

	Research Intern, Spring 2021 Abacus.AI, San Francisco, USA Host: Colin White Research on multi-fidelity AutoML.
	Applied Machine Learning Intern, Summer 2020 ByteDance Inc., Mountain View, USA Host: Ming Chen, Youlong Cheng Neural architecture search for large scale advertising models.
	Research Intern, Summer 2019 Bosch Research, Sunnyvale, USA Host: Huan Song, Liu Ren Unsupervised domain adaptation with image and time-series data.
	Research Intern, Summer 2017 eBay Research, Aachen, Germany Host: Shahram Khadivi, Evgeny Matusov Adapt neural machine translation to e-commerce domains.

Service

	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
	TA for Bachelor, Kinect Programming, Fall 2015

Talks

New Insights in Neural Architecture Search, UT Austin, Dec 2021

This guy makes a nice webpage.