This method applies Polya-Gamma latent variables as a way to obtain closed form expressions for full-conditionals of posterior distributions in sampling algorithms like MCMC.

**AutoDropout** automates the process of designing [dropout](https://paperswithcode.com/method/dropout) patterns using a [Transformer](https://paperswithcode.com/method/transformer) based controller. In this method, a controller learns to generate a dropout pattern at every channel and layer of a target network, such as a [ConvNet](https://paperswithcode.com/methods/category/convolutional-neural-networks) or a Transformer. The target network is then trained with the dropped-out pattern, and its resulting validation performance is used as a signal for the controller to learn from. The resulting pattern is applied to a convolutional output channel, which is a common building block of image recognition models.

The controller network generates the tokens to describe the configurations of the dropout pattern. The tokens are generated like words in a language model. For every layer in a ConvNet, a group of 8 tokens need to be made to create a dropout pattern. These 8 tokens are generated sequentially. In the figure above, size, stride, and repeat indicate the size and the tiling of the pattern; rotate, shear_x, and shear_y specify the geometric transformations of the pattern; share_c is a binary deciding whether a pattern is applied to all $C$ channels; and residual is a binary deciding whether the pattern is applied to the residual branch as well. If we need $L$ dropout patterns, the controller will generate $8L$ decisions.

AutoDropout

AutoDropout: Learning Dropout Patterns to Regularize Deep Networks

Polya-Gamma Augmentation

Bayesian inference for logistic models using Polya-Gamma latent variables

**NEAT**, or **Neural Attention Fields**, is a feature representation for end-to-end imitation learning models. NEAT is a continuous function which maps locations in Bird's Eye View (BEV) scene coordinates to waypoints and semantics, using intermediate attention maps to iteratively compress high-dimensional 2D image features into a compact representation. This allows the model to selectively attend to relevant regions in the input while ignoring information irrelevant to the driving task, effectively associating the images with the BEV representation. Furthermore, visualizing the attention maps for models with NEAT intermediate representations provides improved interpretability.

Source	Bayesian inference for logistic models using Polya-Gamma latent variables
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com