A Primal-Dual Framework for Transformers and Neural Networks. Special Session on "Mathematics of Machine Learning", Canadian Mathematical Society Winter Meeting, 2022. (Invited talk)
Transformer with Fourier Integral Attentions. Deep Learning for Sequence Modeling Minisymposium, the SIAM Conference on Computational Science and Engineering (CSE), 2023. (Invited talk)
Principled Models for Machine Learning. Math Machine Learning Seminar, the Max Planck Institute for Mathematics in the Sciences and UCLA, Applied Math Colloquium at UCLA, 2022. (Invited talk)
Transformer with a Mixture of Gaussian Keys. Geometry of Machine Learning, the 4th Annual Meeting of the SIAM Texas-Louisiana Section, 2021. (Invited talk)
Momentum-Based and Fast Multipole Methods for Designing Deep Learning Models. Mathematical Foundation of Deep Learning with the Applications to PDE, the 4th Annual Meeting of the SIAM Texas-Louisiana Section, 2021. (Invited talk)
Brain-inspired Robust Vision Using Convolutional Neural Networks with Feedback. NeurIPS NeuroAI Workshop, 2019. (Poster)
Conditional Continuous Normalizing Flows for Physics-Inspired Learning. NVIDIA Onsite Research Event, 2019. (Lightning Talk)
Neural Rendering Model: Rethinking Neural Networks from the Joint Generation and Prediction Perspective. NeurIPS Workshop on Integration of Deep Learning Theories, 2018. (Contributed talk)
EnergyNet: Energy-Eď¬€icient Dynamic Inference. NeurIPS Workshop on Compact Deep Neural
Network Representation with Industrial Applications, 2018. (Poster)
Tremor Generative Adversarial Network (TremorGAN): Deep Generative Model Approach for Geophysical Signal Generation. NeurIPS Workshop on Machine Learning for Geophysical and Geochemical Signals, 2018. (Poster)
The Latent-Dependent Deep Rendering Model. ICML Workshop on Theoretical Foundations and Applications of Deep Generative Models, 2018. (Poster)
Mixed Reality Generative Adversarial Networks: Closing the Visual Gap between Synthetic and Real Images. Amazon Graduate Research Symposium, 2017. (Poster)
A Probabilistic Framework for Deep Learning. Computational and System Neuroscience Conference (COSYNE), 2016. (Poster)