**$n$-step Returns** are used for value function estimation in reinforcement learning. Specifically, for $n$ steps we can write the complete return as:

$$ R\_{t}^{(n)} = r\_{t+1} + \gamma{r}\_{t+2} + \cdots + \gamma^{n-1}\_{t+n} + \gamma^{n}V\_{t}\left(s\_{t+n}\right) $$

We can then write an $n$-step backup, in the style of TD learning, as:

$$ \Delta{V}\_{r}\left(s\_{t}\right) = \alpha\left[R\_{t}^{(n)} - V\_{t}\left(s\_{t}\right)\right] $$

Multi-step returns often lead to faster learning with suitably tuned $n$.

Image Credit: Sutton and Barto, Reinforcement Learning

**MODERN**, or **Modulated Residual Network**, is an architecture for [visual question answering](https://paperswithcode.com/task/visual-question-answering) (VQA). It employs [conditional batch normalization](https://paperswithcode.com/method/conditional-batch-normalization) to allow a linguistic embedding from an [LSTM](https://paperswithcode.com/method/lstm) to modulate the [batch normalization](https://paperswithcode.com/method/batch-normalization) parameters of a [ResNet](https://paperswithcode.com/method/resnet). This enables the linguistic embedding to manipulate entire feature maps by scaling them up or down, negating them, or shutting them off, etc.

MODERN

Modulating early visual processing by language

N-step Returns

**Point-GNN** is a graph neural network for detecting objects from a LiDAR point cloud. It predicts the category and shape of the object that each vertex in the graph belongs to. In Point-GNN, there is an auto-registration mechanism to reduce translation variance, as well as a box merging and scoring operation to combine detections from multiple vertices accurately.

Year	2000
Data Source	CC BY-SA - https://paperswithcode.com