FastGCN is a fast improvement of the GCN model recently proposed by Kipf & Welling (2016a) for learning graph embeddings. It generalizes transductive training to an inductive manner and also addresses the memory bottleneck issue of GCN caused by recursive expansion of neighborhoods. The crucial ingredient is a sampling scheme in the reformulation of the loss and the gradient, well justified through an alternative view of graph convoluntions in the form of integral transforms of embedding functions.

Description and image from: [FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling](https://arxiv.org/pdf/1801.10247.pdf)

**ParaNet** is a non-autoregressive attention-based architecture for text-to-speech, which is fully convolutional and converts text to mel spectrogram. ParaNet distills the attention from the autoregressive text-to-spectrogram model, and iteratively refines the alignment between text and spectrogram in a layer-by-layer manner. The architecture is otherwise similar to [Deep Voice 3](https://paperswithcode.com/method/deep-voice-3) except these changes to the decoder; whereas the decoder of DV3 has multiple attention-based layers, where each layer consists of a
[causal convolution](https://paperswithcode.com/method/causal-convolution) block followed by an attention block, ParaNet has a single attention block in the encoder.

ParaNet

Non-Autoregressive Neural Text-to-Speech

FastGCN

FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling

**GMVAE**, or **Gaussian Mixture Variational Autoencoder**, is a stochastic regularization layer for [transformers](https://paperswithcode.com/methods/category/transformers). A GMVAE layer is trained using a 700-dimensional internal representation of the first MLP layer. For every output from the first MLP layer, the GMVAE layer first computes a latent low-dimensional representation sampling from the GMVAE posterior distribution to then provide at the output a reconstruction sampled from a generative model.

Source	FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com