Contents
DS
DS1 Physics Informed Neural Networks • Nov 18, 2021
DS1 Discovering Equations • Nov 18, 2021
GAN
GAN5 Understanding the Connection Between Privacy and Generalization in Generative Adversarial Networks • Oct 28, 2021
GAN5 Generalization and Equilibrium in Generative Adversarial Nets (GANs) • Oct 28, 2021
GAN4 Which Training Methods for GANs do actually Converge? （ICML 2018) • Oct 26, 2021
GAN4 Toward a Better Global Loss Landscape of GANs • Oct 26, 2021
GAN3 Wasserstein GANs • Oct 21, 2021
GAN3 TOWARDS PRINCIPLED METHODS FOR TRAINING GENERATIVE ADVERSARIAL NETWORKS • Oct 21, 2021
GAN2 Cycle-GAN • Oct 19, 2021
GAN2 Understanding Deep Convolutional Generative Adversarial Networks • Oct 19, 2021
GAN1 f-GAN-Training Generative Neural Samplers using Variational Divergence Minimization • Oct 14, 2021
NODE
NODE1 Neural ODE • Nov 2, 2021
Normalizing Flows
NODE2 Augmented Neural ODE • Nov 4, 2021
NF4 Discrete Flows - Invertible Generative Models of Discrete Data • Oct 12, 2021
Physics+ML
DS1 Physics Informed Neural Networks • Nov 18, 2021
DS1 Discovering Equations • Nov 18, 2021
PixelRNN
AR2 Pixel recurrent neural networks • Sep 9, 2021
AR2 Pixel recurrent neural networks • Sep 9, 2021
Privacy
GAN5 Understanding the Connection Between Privacy and Generalization in Generative Adversarial Networks • Oct 28, 2021
Quantiles
AR2 Pixel recurrent neural networks • Sep 9, 2021
AR2 Pixel recurrent neural networks • Sep 9, 2021
AR3 Attention Is All You Need • Sep 14, 2021
AR3 Generating Long Sequences with Sparse Transformers • Sep 14, 2021
AR3 An image is 16 x 16 words • Sep 14, 2021
Variational Autoencoder
VAE4 Can VAE learn concepts from data unsupervised? • Sep 28, 2021
VAE4 Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations • Sep 28, 2021
VAE3 Deep Hierarchical • Sep 23, 2021
VAE2 Improved Inference, Representation • Sep 21, 2021
VAE2 Diagnosing and Enhancing VAE Models • Sep 21, 2021
VAE1 Importance Weighted Autoencoders • Sep 16, 2021