**rnnDrop** is a [dropout](https://paperswithcode.com/method/dropout) based regularization technique for [recurrent neural networks](https://paperswithcode.com/methods/category/recurrent-neural-networks). It amounts to using the same dropout mask at every timestep. It drops both the non-recurrent and recurrent connections. A simple figure to explain the idea is shown to the right. The figure shows an RNN being trained with rnnDrop for three frames $\left(t-1, t, t+1\right)$ on two different training sequences in the data (denoted as ‘sequence1’ and ‘sequence2’). The black circles denote the randomly omitted hidden nodes during training, and the dotted arrows stand for the model weights connected to those omitted nodes.

*From: RnnDrop: A Novel Dropout for RNNs in ASR by Moon et al*

In machine learning, a hyperparameter is a parameter whose value is used to control learning process, and HPO is the problem of choosing a set of optimal hyperparameters for a learning algorithm.

Algorithms for Hyper-Parameter Optimization

rnnDrop

**Path Aggregation Network**, or **PANet**, aims to boost information flow in a proposal-based instance segmentation framework. Specifically, the feature hierarchy is enhanced with accurate localization signals in lower layers by [bottom-up path augmentation](https://paperswithcode.com/method/bottom-up-path-augmentation), which shortens the information path between lower layers and topmost feature. Additionally, [adaptive feature pooling](https://paperswithcode.com/method/adaptive-feature-pooling) is employed, which links feature grid and all feature levels to make useful information in each feature level propagate directly to following proposal subnetworks. A complementary branch capturing different views for each proposal is created to further improve mask prediction.

Year	2015
Data Source	CC BY-SA - https://paperswithcode.com