**BiGG** is an autoregressive model for generative modeling for sparse graphs. It utilizes sparsity to avoid generating the full adjacency matrix, and reduces the graph generation time complexity to $O(((n + m)\log n)$. Furthermore, during training this autoregressive model can be parallelized with $O(\log n)$ synchronization stages, which makes it much more efficient than other autoregressive models that require $\Omega(n)$. The approach is based on three key elements: (1) an $O(\log n)$ process for generating each edge using a binary tree data structure, inspired by R-MAT; (2) a tree-structured autoregressive model for generating the set of edges associated with each node; and (3) an autoregressive model defined over the sequence of nodes.

**WaveGrad** is a conditional model for waveform generation through estimating gradients of the data density. This model is built on the prior work on score matching and diffusion probabilistic models. It starts from Gaussian white noise and iteratively refines the signal via a gradient-based sampler conditioned on the mel-spectrogram. WaveGrad is non-autoregressive, and requires only a constant number of generation steps during inference. It can use as few as 6 iterations to generate high fidelity audio samples.

WaveGrad

WaveGrad: Estimating Gradients for Waveform Generation

BiGG

Scalable Deep Generative Modeling for Sparse Graphs

**wav2vec-U** is an unsupervised method to train speech recognition models without any labeled data. It leverages self-supervised speech representations to segment unlabeled language and learn a mapping from these representations to phonemes via adversarial training. 

Specifically, we learn self-supervised representations with wav2vec 2.0 on unlabeled speech audio, then identify clusters in the representations with k-means to segment the audio data. Next, we build segment representations by mean pooling the wav2vec 2.0 representations, performing [PCA](https://paperswithcode.com/method/pca) and a second mean pooling step between adjacent segments. This is input to the generator which outputs a phoneme sequence that is fed to the discriminator, similar to phonemized unlabeled text to perform adversarial training.

Source	Scalable Deep Generative Modeling for Sparse Graphs
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com