**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.

**Dorylus** is a distributed system for training graph neural networks which uses cheap CPU servers and Lambda threads. It scales to
large billion-edge graphs with low-cost cloud resources.

Dorylus

Dorylus: Affordable, Scalable, and Accurate GNN Training with Distributed CPU Servers and Serverless Threads

MODERN

Modulating early visual processing by language

**$n$-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

Source	Modulating early visual processing by language
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com