README
Repository for the JWZ group.
1 A tutorial resource for VAEs emphasizing the mathematical foundation
2 A python package for summarizing your models similar to the Keras .summary() method. Download the package into your main project wd e.g
--Src
--Models
myClass.py
--pytorch-summary-master
3 VAE with a modified mixture of variationals prior (VampPrior)
4 VAE with a modified mixture of variationals prior (VampPrior) github page
5 Beta-VAE paper. Also discussing the notions of disentanglement
6 InfoVAE paper covering maximization of mutual information between inputs and intermediate codes a la InfoGAN techniques.
7 "A General and Adaptive Robust Loss Function." Worth a read. Seems like a very interesting dynamic loss function to use. It can be altered and scheduled to smoothly interpolate between various loss functions which occur as special cases of this one.
8 A very good explanation of computational graphs.
9 Github repository for generating moving digits. We will use these scripts to generate train/test data for our video based generative models. We will be able to control key independent generative features and hence be able to see if our model truly captures critical features.
10 Basic VAE example in PyTorch
11 Basic intro to Transpose Conv
12 Basic intro to Transpose Conv
13 Basic intro to Conv arithmatic of all forms
14 Introduction to data topology
15 LSTM tutorial
16 Spectral clustering tutorial
17 Eigen-cuts algorithm
18 Utility package we ought to implement
19 Two-stream dynamic texture synthesis
20 MONet: Unsupervised scene decomposition and representation
21 Spatial Broadcast Decoder
22 IODINE Network
23 Augmented MNIST data set, including texture-in-texture generation
24 Further explanation for VAE objective functino, ELBO and loss
25 Paper on using compressed representation of video data to truncate noise from low-frequency motion
26 Paper exploring the idea and implementation of general homemorphic manifolds extending the standard guassian prior
27 Paper creating an model which operates on clustering/classifying sets as objects rather than vectors
28 Paper on the equivariance of models (through parameter sharing) using resistance to group actions
29 "PROBABILISTIC SYMMETRY AND INVARIANT NEURAL NETWORKS"
30 "Deformable Convolutional Networks"
31 "ON LARGE-BATCH TRAINING FOR DEEP LEARNING: GENERALIZATION GAP AND SHARP MINIMA"
32 Paper exploring (fast) time-series clustering
33 B3DF paper
34 MonNet architecture, which is a good look at iterative refinmenet in models, along with a great introduction to the utility of masks
35 IODINE network architecture, which is an evolutoin of
36 High-fidelity reconstruction using discretized encoding vectors