**CRF-RNN** is a formulation of a [CRF](https://paperswithcode.com/method/crf) as a Recurrent Neural Network. Specifically it formulates mean-field approximate inference for the Conditional Random Fields with Gaussian pairwise potentials as Recurrent Neural Networks.

The **Invertible 1x1 Convolution** is a type of [convolution](https://paperswithcode.com/method/convolution) used in flow-based generative models that reverses the ordering of channels. The weight matrix is initialized as a random rotation matrix. The log-determinant of an invertible 1 × 1 convolution of a $h \times w \times c$ tensor $h$ with $c \times c$ weight matrix $\mathbf{W}$ is straightforward to compute:

$$ \log | \text{det}\left(\frac{d\text{conv2D}\left(\mathbf{h};\mathbf{W}\right)}{d\mathbf{h}}\right) | = h \cdot w \cdot \log | \text{det}\left(\mathbf{W}\right) | $$

Invertible 1x1 Convolution

Glow: Generative Flow with Invertible 1x1 Convolutions

CRF-RNN

Conditional Random Fields as Recurrent Neural Networks

**VEGA** is an AutoML framework that is compatible and optimized for multiple hardware platforms. It integrates various modules of AutoML, including [Neural Architecture Search](https://paperswithcode.com/method/neural-architecture-search) (NAS), Hyperparameter Optimization (HPO), Auto Data Augmentation, Model Compression, and Fully Train. To support a variety of search algorithms and tasks, it involves a fine-grained search space and a description language to enable easy adaptation to different search algorithms and tasks.

Source	Conditional Random Fields as Recurrent Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com