Recent years have seen considerable advances in generative models, which learn distributions from data and also generate new data instances from the learned distribution; and dynamical models, which model systems with a dynamical or temporal component. Both of these developments have been leveraging advances in deep learning. The course will cover key advances in generative and dynamical models, including variational auto-encoders, normalizing flows, generative adversarial networks, neural differential equations, physics guided machine learning, among other topics.
This link for course website is at https://arindam.cs.illinois.edu/courses/f21cs598/
Posts
VAE4 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations
VAE3 Deep Hierarchical
VAE2 Improved Inference, Representation
VAE2 Diagnosing and Enhancing VAE Models
VAE1 Importance Weighted Autoencoders
AR3 Attention Is All You Need
AR3 Generating Long Sequences with Sparse Transformers
AR3 An image is 16 x 16 words
AR2 Pixel recurrent neural networks
AR2 Pixel recurrent neural networks
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