- [2024/12] One paper was accepted to AAAI 2025.
- [2024/09] One paper was accepted to NeurIPS 2024.
- [2024/07] I gave a talk about "Efficient Large Models" at Peking University. Many thanks to Prof. Shanghang Zhang for the invitation.
- [2024/07] Two papers were accepted to ECCV 2024.
- [2024/06] I co-organized TNNLS 2024 Tutorial "Efficient and Secure Foundation Models" and gave a talk.
- [2024/06] One paper was accepted to journal Knowledge-Based Systems.
- [2024/03] Our PU-MLC was accepted to ICME 2024 as oral.
- [2024/03] We released ActGen & LocalMamba & EfficientVMamba at arXiv.
- [2024/02] One paper (FreeKD) was accepted to CVPR 2024.
- [2023/09] One paper (DiffKD) was accepted to NeurIPS 2023.
- [2023/07] One paper (AKD) was accepted to ACM MM 2023.
- [2023/05] We released DiffKD and AKD at arXiv.
- [2023/01] One paper (MasKD) was accepted to ICLR 2023.
- [2022/09] One paper (DIST) was accepted to NeurIPS 2022.
- [2022/07] We released LightViT [code] at arXiv.
- [2022/05] We released DIST [code] and MasKD [code] at arXiv.
- [2022/04] One paper (QAS) was accepted to Nature Partner Journals Quantum Information (NPJ QI).
- [2022/03] Two papers (GreedyNASv2 & DyRep) were accepted to CVPR 2022.
- [2022/01] One paper (ReLoss) was accepted to ICLR 2022.
-
[2021/12] We released
MMRazor - a model compression toolkit for model slimming and AutoML, which includes 3 mainstream technologies NAS, pruning, and KD. MMRazor can be easily applied to various projects (e.g., MMDet and MMCls) in OpenMMLab. -
[2021/11] We released GreedyNASv2 at
arXiv. -
[2021/03] One paper about NAS was accepted to CVPR 2021. The NAS benchmark in our paper was released at
github. - [2021/01] One paper about channel number search (pruning) was accepted to ICLR 2021 as spotlight.
-
[2020/11] One paper about NAS was released at
arXiv. Our TopoNAS explicitly learns the topology for differentiable NAS (DARTS), and enjoys significant efficiency improvement on obtained architectures. -
[2020/10] One paper about quantum architecture search (QAS) was released at
arXiv. Our QAS implicitly learns a rule that can well suppress the influence of quantum noise and the barren plateau. - [2020/02] One paper about NAS was accepted to CVPR 2020.
Biography
My name is Tao Huang (黄涛). I am a tenure-track assistant professor in John Hopcropt Center for Computer Science, School of Computer Science, Shanghai Jiao Tong University since 2025. I received my Ph.D. degree in Computer Science at The University of Sydney, advised by Prof. Chang Xu.
Before that, I obtained the Bachelor's degree at Huazhong University of Science and Technology, and then spent two years as a Researcher at SenseTime.
Our team conducts research across a broad range of topics, including LLMs, VLMs, image and video generation, and embodied AI. My primary research interests during my PhD focus on efficient deep learning algorithms and their applications to computer vision, with an emphasis on:
- Efficient architectures: NAS, handcrafts ...
- Efficient training: training acceleration, KD, PEFT ...
- Efficient data: self-supervised learning, generated training contents ...
- Efficient inference: pruning, dynamic networks ...
- Efficient large models: VLMs, Generative Models, VLAs ...
Tao Huang
Assistant Professor, PhD Advisor
Shanghai Jiao Tong University
Email: t.huang[at]sjtu.edu.cn
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