Yuhuang Hu

LatticeFlow AG

1. Introduction

Go player, (fake) Hiker, (fake) Biker, (not anymore) TV addict, AGI supporter, NOT-DOOMED believer, (retired) LaTeX tutor, (former) INI Ph.D. student, Senior Machine Learning Engineer at LatticeFlow AG.

For more information, please click here for my current CV.

2. Method

2.1 Research Interests

  • Deep Neural Networks
  • Event-based Learning and Processing
  • Feature Learning
  • Computer Vision
  • Robotic Vision
  • Reliable and Robust AI
  • Artificial General Intelligence
  • Theory of Everything

2.2 TypeRacer

TypeRacer.com scorecard for user duguyue100

2.3 Coding Time

Total time coded since Aug 3 2017

3. Experiments

Python C/C++ GPU HTML Git SVN Emacs Vim/neovim zsh Linux Mac OS X CMake DVS DAViS LaTeX macman Midnight Commander

4. Conclusions


Computer Science Mathematics Machine Learning Artificial Intelligence Pretty PDFs \(\LaTeX\) Git Reasoning Intellectual Debates Moral Consequence Robot History Future Bejing Opera Markdown Marvel Ice-cream Smart Girl Engineer Physics TV Shows Cats and Dogs pics Scientific American Being minority GSoC Accidentally find a nice song Tom Hanks in girl's voice Chinese Calligraphy Surprising ending Reading Endless Article Machine Learning in Browser Write in Emacs Recurrent Neural Networks Vim\neovim Terminal

4.2 Dislikes

Low Network Speed Cannot Explain Myself Precisely Another Meeting Delay Know-it-all Fake Social Manners Around with People who I don't like All kinds of reunion Tourism Ambush my back Exams Intentional delay Birthday Sending gifts Things out of control New guy Meaningless date Boring lecture Select gifts Season Finale Have no idea about what's next Coders who don't follow standards Person who refuses to learn useful things


  1. Y. Hu, T. Delbruck, S-C. Liu, “Learning to Exploit Multiple Vision Modalities by Using Grafted Networks” in The 16th European Conference on Computer Vision (ECCV), Virtual, 2020.

  2. Y. Hu, J. Binas, D. Neil, S-C. Liu, T. Delbruck, “DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction” in Special session Beyond Traditional Sensing for Intelligent Transportation, The 23rd IEEE International Conference on Intelligent Transportation Systems (ITSC) Rhodes, Greece, 2020.

  3. Y. Gao, N.I. Nikolov, Y. Hu, R.H.R. Hahnloser, “Character-Level Translation with Self-attention” in 2020 Annual Conference of the Association for Computational Linguistics (ACL), Seattle, Washington, 2020.

  4. I.A. Lungu, Y. Hu, S-C. Liu, “Multi-Resolution Siamese Networks for One-Shot Learning” in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genoa, Italy, 2020.

  5. S. Wang, Y. Hu, J. Burgués, S. Macro, S-C. Liu, “Prediction of Gas Concentration Using Gated Recurrent Neural Networks” in 2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Genoa, Italy, 2020.

  6. Y. Hu, T. Delbruck, S-C. Liu, “Incremental Learning Meets Reduced Precision Networks” in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), Sapporo, Japan, 2019.

  7. Y. Hu, H.M. Chen, T. Delbruck, “Slasher: Stadium racer for end-to-end event-based camera autonomous driving experiments” in 2019 IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS), Hsinchu, Taiwan, 2019.

  8. N.I. Nikolov, Y. Hu, MX. Tan, R.H.R. Hahnloser, “Character-level Chinese-English Translation through ASCII Encoding” in The Third Conference on Machine Translation (WMT18), Brussels, Belgium, 2018.

  9. B. Rueckauer, I-A. Lungu, Y. Hu, M. Pfeiffer, S-C. Liu, “Conversion of Continuous-Valued Deep Networks to Efficient Event-Driven Networks for Image Classification”, Frontiers in Neuroscience, 11:682, 2017.

  10. B. Reckauer, I-A. Lungu, Y. Hu, M. Pfeiffer, “Theory and Conversion of Analog to Spiking Convolutional Neural Netowrks” in Computing with Spikes NIPS 2016 Workshop, Barcelona, Spain, 2016.

  11. Y. Hu, H. Liu, M. Pfeiffer, T. Delbruck, “DVS Benchmark Datasets for Object Tracking, Action Recognition and Object Recognition”, Frontiers in Neuroscience, 10:405, 2016.

  12. D.N.T. How, Y. Hu, K.S.M. Sahari, C.K. Loo, “ Multiple Sequence Behavior Recognition on Humanoid Robot using Long Short-term Memory (LSTM)” in 2014 IEEE International Symposium on Robotics and Manufacturing Automation (ROMA 2014), Kuala Lumpur, Malaysia, 2014.

  13. Y. Hu, D.N.T. How, K.S.M. Sahari, C.K. Loo, “Learning Sufficient Representation for Spatio-temporal Deep Network Using Information Filter” in 2014 IEEE/SICE International Symposium on System Integration, Korakuen Campus, Chuo University, Tokyo, Japan, 2014.

  14. Y. Hu, C.K. Loo, “A Generalized Quantum-Inspired Decision Making Model for Intelligent Robot”, The Scientific World Journal, vol. 2014, Article ID 240983, 8 pages, 2014.

  15. Y. Hu, W.L. Hoo, C.S. Chan, “Clustering Algorithms for Scene Classification — A Performance Comparison between K-means; Fuzzy c-means and GMM” in IIEEJ 3rd Image Electronics and Visual Computing Workshop, Kuching, Malaysia, 2012, IIEEJ.