Please enter a description about the method here

**GLM** is a bilingual (English and Chinese) pre-trained transformer-based language model that follow the traditional architecture of decoder-only autoregressive language modeling. It leverages autoregressive blank infilling as its training objective.

GLM-130B: An Open Bilingual Pre-trained Model

SSFG regularization

SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks

**VQ-VAE-2** is a type of variational autoencoder that combines a a two-level hierarchical VQ-[VAE](https://paperswithcode.com/method/vae) with a self-attention autoregressive model ([PixelCNN](https://paperswithcode.com/method/pixelcnn)) as a prior. The encoder and decoder architectures are kept simple and light-weight as in the original [VQ-VAE](https://paperswithcode.com/method/vq-vae), with the only difference that hierarchical multi-scale latent maps are used for increased resolution.

Source	SSFG: Stochastically Scaling Features and Gradients for Regularizing Graph Convolutional Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com