jilin AT mit.edu
I am currently a third-year Ph.D. student at MIT EECS, advised by Prof. Song Han. My research interests lie in efficient deep learning algorithms and systems.
I received my B.Eng. in Electronic Engineering from Tsinghua University, and M.Sc. in EECS from MIT. I interned at Adobe Research with Jun-Yan Zhu and Richard Zhang during Summer 2020, and worked at OmniML during summer 2021.
* indicates equal contribution
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MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
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Anycost GANs for Interactive Image Synthesis and Editing
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MCUNet: Tiny Deep Learning on IoT Devices
Press:
MIT News (homepage spotlight) /
WIRED /
MIT TR-China /
IBM /
Morning Brew /
Stacey on IoT /
Analytics Insight /
Techable /
Tendencias
Spotlight Presentation
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Differentiable Augmentation for Data-Efficient GAN Training
Press:
VentureBeat
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GAN Compression: Efficient Architectures for Interactive Conditional GANs
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APQ: Joint Search for Network Architecture, Pruning and Quantization Policy
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AutoML for Architecting Efficient and Specialized Neural Networks
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TSM: Temporal Shift Module for Efficient Video Understanding
ICCV 2019 /
arXiv
Training Kinetics in 15 Minutes: Large-scale Distributed Training on Videos
Press:
MIT News /
MIT Technology Review /
WIRED /
Engadget/
NVIDIA News /
Industry Integration@NVIDIA
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HAQ: Hardware-Aware Automated Quantization
Oral Presentation
Hardware-Centric AutoML for Mixed-Precision Quantization
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Defensive Quantization: When Efficiency Meets Robustness
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AMC: AutoML for Model Compression and Acceleration on Mobile Devices
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Runtime Neural Pruning
Runtime Network Routing for Efficient Image Classification
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