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 professors Serena Yeung, C. Karen Liu, and Scott Delp. I did my Ph.D. at the University of Toronto advised by professor Rich Zemel.


Publications

2024

  1. NeurIPS
    "Interpreting the Weight Space of Customized Diffusion Models" Amil Dravid, Yossi Gandelsman, Kuan-Chieh Wang, Rameen Abdal, Gordon Wetzstein, Alexei A Efros, and Kfir Aberman In NeurIPS 2024 πŸ“„ Paper 🌐 Project Page
  2. SIGA
    "MoA: Mixture-of-Attention for Subject-Context Disentanglement in Personalized Image Generation" Kuan-Chieh Wang, Daniil Ostashev, Yuwei Fang, Sergey Tulyakov, and Kfir Aberman In SIGGRAPH Asia 2024 πŸ“„ Paper 🌐 Project Page
  3. ECCV
    "Viewpoint Textual Inversion: Unleashing Novel View Synthesis with Pretrained 2D Diffusion Models" James Burgess, Kuan-Chieh Wang, and Serena Yeung In ECCV 2024 πŸ“„ Paper 🌐 Project Page πŸ›  Code
  4. SIGGRAPH
    "Iterative Motion Editing with Natural Language" Purvi Goel, Kuan-Chieh Wang, C Karen Liu, and Kayvon Fatahalian In SIGGRAPH 2024 πŸ“„ Paper 🌐 Project Page

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
    "Learning the Stein Discrepancy for 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

Preprints

  1. preprint
    "Open World Object Detection in the Era of Foundation Models" Orr Zohar, Alejandro Lozano, Shelly Goel, Serena Yeung-Levy, and Kuan-Chieh Wang In 2023 πŸ“„ Paper 🌐 Project Page πŸ›  Code



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.