Please enter a description about the method here

**Parallax** is a hybrid parallel method for training large neural networks. Parallax is a framework that optimizes data parallel training by utilizing the sparsity of model parameters. Parallax introduces a hybrid approach that combines Parameter Server and AllReduce architectures to optimize the amount of data transfer according to the sparsity.

Parallax pursues a hybrid approach that uses the Parameter Server architecture for handling sparse variables and the AllReduce architecture for handling dense variables. Moreover, Parallax partitions large sparse variables by a near-optimal number of partitions to maximize parallelism while maintaining low computation and communication overhead. Parallax further optimizes training with local aggregation and smart operation placement to mitigate communication overhead. Graph transformation in Parallax automatically applies all of these optimizations and the data parallel training itself at the framework level to minimize user efforts for writing and optimizing a distributed program by composing low-level primitives.

Parallax

VocGAN

VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network

**VIME **, or **Value Imputation and Mask Estimation**, is a self- and semi-supervised learning framework for tabular data. It consists of a pretext task of estimating mask vectors from corrupted tabular data in addition to the reconstruction pretext task for self-supervised learning.

Source	VocGAN: A High-Fidelity Real-time Vocoder with a Hierarchically-nested Adversarial Network
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com