A **TResNet** is a variant on a [ResNet](https://paperswithcode.com/method/resnet) that aim to boost accuracy while maintaining GPU training and inference efficiency.  They contain several design tricks including a SpaceToDepth stem, [Anti-Alias downsampling](https://paperswithcode.com/method/anti-alias-downsampling), In-Place Activated BatchNorm, Blocks selection and squeeze-and-excitation layers.

A **Stochastic Dueling Network**, or **SDN**, is an architecture for learning a value function $V$. The SDN learns both $V$ and $Q$ off-policy while maintaining consistency between the two estimates. At each time step it outputs a stochastic estimate of $Q$ and a deterministic estimate of $V$.

Stochastic Dueling Network

Sample Efficient Actor-Critic with Experience Replay

TResNet

TResNet: High Performance GPU-Dedicated Architecture

Learnable graph convolutional layer (LGCL) automatically selects a fixed number of neighboring nodes for each feature based on value ranking in order to transform graph data into grid-like structures in 1-D format, thereby enabling the use of regular convolutional operations on generic graphs.

Description and image from: [Large-Scale Learnable Graph Convolutional Networks](https://arxiv.org/pdf/1808.03965.pdf)

Source	TResNet: High Performance GPU-Dedicated Architecture
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com