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Jiaxin Shi

Talk Slides

  • Discrete Generative Modeling with Masked Diffusions
  • Designing Sequence Models with Wavelets and Multiresolution Convolutions
  • Stein’s Method for Modern Machine Learning: From Gradient Estimation to Generative Modeling
  • Spectral Methods and Generative Modeling: A Unifying Perspective
  • Differentiable Programming in Probabilistic Models
  • Sampling with Mirrored Stein Operators
  • Neural Networks as Inter-Domain Inducing Points
  • Function-Space Orthogonality in Probabilistic Learning
  • Sparse Orthogonal Variational Inference for Gaussian Processes
  • Inference Networks for Gaussian Processes
  • A Spectral Approach to Gradient Estimation for Implicit Distributions

Notes

  • Natural Gradient, Variational Inference, and Catastrophic Forgetting
  • Dimension Reduction and Nyström Methods
  • Notes on Deep Belief Networks (DBN)