My course lecture on Diffusion Models from statistical first principles:
dropbox.com/scl/fi/gmwhx7j…
The PyTorch notebooks that implement Diffusion Models from scratch w/ Transformer and UNet:
github.com/xbresson/CS524…
Sharing my lecture slides on "Recent Developments of Graph Network Architectures" from my deep learning course. It is a review of some exciting works on GNNs published in 2019-2020. #feelthelearnrb.gy/quo3n6
Sharing my lecture slides on Attention Nets/Transformers with two simple codes for (1) Language Modeling and (2) Sequence-To-Sequence Modeling to understand Transformers from scratch.
Slides : dropbox.com/s/rahrg6s7w4vu…
Codes : github.com/xbresson/CS524…
Happy to deliver a remote lecture on "Graph Convolutional Networks" tomorrow for the NYU Deep Learning course of @ylecun and @alfcnz. Slides and video will be made available.
My lecture notes on regularization techniques in machine learning
dropbox.com/scl/fi/ivyb3ws…
Check out the section on double descent suggested by @ylecun :)
Our paper "Benchmarking Graph Neural Networks" has been accepted for publication at Journal of Machine Learning Research @JmlrOrg!
arxiv.org/pdf/2003.00982…
(after rejection from NeurIPS, ICLR and ICML :)
My 10 Favorite Algorithms
• K-means and spectral clustering
• FFT
• Random forest and gradient boosting
• Personalized PageRank
• ADMM and primal-dual optimization
• EVD and SVD
• Backpropagation and SGD
• Convnet and transformer
• Reinforce algorithm
• Diffusion model
