Yuhuang Hu

Position:	PhD Student -- ended Mar 2022
Email:	yuhu@ini.uzh.ch
Work phone:
Home page:	http://dgyblog.com/

Before I joined INI as an NSC master student, I graduated from Faculty of Computer Science & IT, University of Malaya, Kuala Lumpur, Malaysia in 2015. I obtained my Computer Science degree with a specialization in Artificial Intelligence. I completed my journey as a master student in 2017. Now I'm a Ph.D. student in Sensors group.

My current research includes Deep Neural Networks, Self-Supervised Learning, Event-based Learning and Processing, Computer Vision. I am also strongly interested in Artificial General Intelligence, and Computational Neuroscience.

Supervisor

Shih-Chii Liu

Publications

2023

Shu Wang, Meritxell Rovira, Yuhuang Hu, Cecilia Jiménez-Jorquera, Shih-Chii Liu End-to-end prediction of sodium concentration from uncalibrated sodium ISFETs, IEEE International Symposium on Circuits and Systems (ISCAS) 1-5, 2023

2022

Shu Wang*, Yuhuang Hu*, Shih-Chii Liu T-NGA: Temporal Network Grafting Algorithm for Learning to Process Spiking Audio Sensor Events, 2022 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP) 3273-3277, 2022
Yuhuang Hu, Shih-Chii Liu Kernel Modulation: A Parameter-Efficient Method for Training Convolutional Neural Networks, 2022 26th International Conference on Pattern Recognition (ICPR), 2022
Zuowen Wang*, Yuhuang Hu*, Shih-Chii Liu Exploiting spatial sparsity for event cameras with visual transformers, International Conference on Image Processing (ICIP), 2022

2021

Hu, Y., Liu, S-C., Delbruck T. v2e: From Video Frames to Realistic DVS Events, 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2021

2020

Hu, Y., Binas, J., Neil, D., Liu, S-C., Delbruck T. DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction, The 23rd IEEE International Conference on Intelligent Transportation Systems, 2020
Hu Y., Delbruck T., Liu, S-C. Learning to Exploit Multiple Vision Modalities by Using Grafted Networks, The 16th European Conference on Computer Vision (ECCV), 2020
Lungu, I. A., Aimar, A., Hu, Y. , Delbruck, T., and Liu, S-C. Siamese networks for few-shot learning on edge embedded devices, IEEE Journal on Emerging and Selected Topics in Circuits and Systems , 10:(4) 488-497, 2020
Lungu, I. A., Hu, Y. and Liu, S-C. Multi-resolution Siamese networks for one-shot learning, 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems 183-187, 2020
Wang, S., Hu, Y., Burgues, J., Marco, S. and Liu, S-C. Prediction of gas concentration using gated recurrent neural networks, 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems 178-182, 2020
Yingqiang Gao, Nikola I. Nikolov, Yuhuang Hu, Richard H.R. Hahnloser Character-level Translation with Self-attention, Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020), 2020

2019

Hu, Y., Delbruck, T., and Liu, S-C. Incremental Learning Meets Reduced Precision Networks, 2019 IEEE International Symposium on Circuits and Systems (ISCAS), 2019
Hu, Yuhuang and Chen, Hong Ming and Delbruck, Tobi Slasher: Stadium racer car for event camera end-to-end learning autonomous driving experiments, 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), 2019

2018

Nikola I. Nikolov, Yuhuang Hu, Mi Xue Tan, Richard H.R. Hahnloser Character-level Chinese-English Translation through ASCII Encoding, Proceedings of the Third Conference on Machine Translation (WMT18), EMNLP 2018, Volume 1: Research Papers:, 2018

2017

Rueckauer, B., Lungu, I., Hu, Y. , Pfeiffer, M., and Liu, S-C. Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification, Frontiers in Neuroscience, 11: 1-12, 2017 SNN_Conversion_Frontiers

2016

Hu Y., Liu H., Pfeiffer M., and Delbruck T. DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition, Frontiers in Neuromorphic Engineering, 10:(405), 2016
Rückauer B., Lungu I-A, Hu Y., and Pfeiffer M. Theory and Tools for the Conversion of Analog to Spiking Convolutional Neural Networks, arXiv, 2016 SNN_Conversion

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