Kuan-Chieh (Jackson) Wang

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Bio

Hi! I am a Research Scientist at Snap Research in Palo Alto, where I work on generative models. My team and I are looking for interns. Feel free to reach out to me directly as well!

Before joining Snap, I was a postdoc at Stanford CS and the Wu Tsai Human Performance Alliance where I worked closely with Prof. Serena Yeung, Prof. C. Karen Liu, and Prof. Scott Delp. I did my Ph.D. at the University of Toronto advised by Prof. Rich Zemel.


Preprints

  1. preprint
    "Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models" James Burgess, Kuan-Chieh Wang, and Serena Yeung In 2023 [🌐 Project Page]


Publications

2023

  1. NeurIPS D&B
    "LOVM: Language-Only Vision Model Selection" Orr Zohar, Shih-Cheng Huang, Kuan-Chieh Wang, and Serena Yeung In NeurIPS Dataset and Benchmark 2023 [πŸ“„ Paper]
  2. ICCV
    "Generalizable Neural Fields as Partially Observed Neural Processes" Jeffrey Gu, Kuan-Chieh Wang, and Serena Yeung In ICCV 2023 [πŸ“„ Paper]
  3. CVPR
    "NeMo: 3D Neural Motion Fields from Multiple Video Instances of the Same Action" Kuan-Chieh Wang, Zhenzhen Weng, Maria Xenochristou, Joao Pedro Araujo, Jeffrey Gu, C Karen Liu, and Serena Yeung In CVPR 2023 -- Highlight (2.5% of submissions) [πŸ“„ Paper] [🌐 Project Page] [πŸ›  Code]
  4. CVPR
    "PROB: Probabilistic Objectness for Open World Object Detection" Orr Zohar, Kuan-Chieh Wang, and Serena Yeung In CVPR 2023 [πŸ“„ Paper] [πŸ›  Code]
  5. ICLR
    "DrML: Diagnosing and Rectifying Vision Models using Language" Yuhui Zhang, Jeff Z HaoChen, Shih-Cheng Huang, Kuan-Chieh Wang, James Zou, and Serena Yeung In ICLR 2023 [πŸ“„ Paper] [πŸ›  Code]

2022

  1. 3DV
    "Domain Adaptive 3D Pose Augmentation for In-the-wild Human Mesh Recovery" Zhenzhen Weng, Kuan-Chieh Wang, Angjoo Kanazawa, and Serena Yeung In 3DV 2022 [πŸ“„ Paper] [πŸ›  Code]
  2. COLLA
    "Disentanglement and generalization under correlation shifts" Christina M Funke, Paul Vicol, Kuan-Chieh Wang, Matthias KΓΌmmerer, Richard Zemel, and Matthias Bethge In Conference on Lifelong Learning Agents 2022 [πŸ“„ Paper]

2021

  1. NeurIPS
    "Variational Model Inversion Attacks" Kuan-Chieh Wang, Yan Fu, Ke Li, Ashish Khisti, Richard Zemel, and Alireza Makhzani In NeurIPS 2021 [πŸ“„ Paper] [πŸ›  Code]
  2. NeurIPS
    "Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation" Jixuan Wang, Kuan-Chieh Wang, Frank Rudzicz, and Michael Brudno In NeurIPS 2021 [πŸ“„ Paper] [πŸ›  Code]
  3. AISTATS
    "Understanding and mitigating exploding inverses in invertible neural networks" Jens Behrmann*, Paul Vicol*, Kuan-Chieh Wang*, Roger B. Grosse, and JΓΆrn-Henrik Jacobsen In AISTATS 2021 [πŸ“„ Paper] [πŸ›  Code]

2020

  1. ICMLw
    "Few-shot Out-of-Distribution Detection" Kuan-Chieh Wang, Paul Vicol, Eleni Triantafillou, and Richard Zemel In ICML Workshop on Uncertainty and Robustness in Deep Learning 2020 -- Spotlight [🌐 HTML] [πŸ›  Code]
  2. ICML
    "Cutting out the Middle-Man: Training and Evaluating Energy-Based Models without Sampling" Will Grathwohl, Kuan-Chieh Wang, Jorn-Henrik Jacobsen, David Duvenaud, and Richard Zemel In ICML 2020 [πŸ“„ Paper] [πŸ›  Code]
  3. ICLR
    "Your classifier is secretly an energy based model and you should treat it like one" Will Grathwohl, Kuan-Chieh Wang*, JΓΆrn-Henrik Jacobsen*, David Duvenaud, Mohammad Norouzi, and Kevin Swersky In ICLR 2020 -- Oral [πŸ“„ Paper] [πŸ›  Code]

2019

  1. ICLRw
    "Customizable Facial Gesture Recognition For Improved Assistive Technology" Kuan-Chieh Wang, Jixuan Wang, Khai Truong, and Richard Zemel In ICLR AI for Social Good Workshop 2019 [🌐 HTML]
  2. ICLRw
    "Towards Few-Shot Out-of-Distribution Detection" Kuan-Chieh Wang*, Chia-Cheng Liu*, Paul Vicol, and Richard Zemel In ICLR Safe Machine Learning Workshop 2019 [🌐 HTML]
  3. "Lingvo: a modular and scalable framework for sequence-to-sequence modeling" Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, and others Technical Report 2019 [πŸ“„ Paper] [πŸ›  Code]
  4. ICASSP
    "Centroid-based deep metric learning for speaker recognition" Jixuan Wang*, Kuan-Chieh Wang*, Marc T Law, Frank Rudzicz, and Michael Brudno In ICASSP 2019 [πŸ“„ Paper]

2018

  1. ICML
    "Adversarial distillation of Bayesian neural network posteriors" Kuan-Chieh Wang, Paul Vicol, James Lucas, Li Gu, Roger Grosse, and Richard Zemel In ICML 2018 [πŸ“„ Paper] [πŸ›  Code]
  2. ICML
    "Neural Relational Inference for Interacting Systems" Thomas Kipf*, Ethan Fetaya*, Kuan-Chieh Wang, Max Welling, and Richard Zemel In ICML 2018 [πŸ“„ Paper] [πŸ›  Code]

2017

  1. NeurIPS
    "Dualing GANs" Yujia Li, Alexander Schwing, Kuan-Chieh Wang, and Richard Zemel In NeurIPS 2017 -- Spotlight [πŸ“„ Paper]

2016

  1. SSAC
    "Classifying NBA offensive plays using neural networks" Kuan-Chieh Wang, and Richard Zemel In MIT Sloan Sports Analytics Conference 2016 [πŸ“„ Paper]


Professional Experience

I was

  • an intern at Google Brain (MTV, TOR) in 2018 hosted by Chung-Cheng Chiu. We worked on speech recognition together.
  • a research student working with the Toronto Raptors’ analysts Keith Boyarsky and Eric Khoury. My mom likes to believe that the Toronto Raptors getting better, and eventually winning the Championship in 2019 had something to do with my research. πŸ€”
  • a ML consultant at SmartFinance LLC, a start-up Rich co-founded. We had some fun trying to make finance easier with ML.