A **ResNeXt** repeats a building block that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension,  *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. 

Formally, a set of aggregated transformations can be represented as: $\mathcal{F}(x)=\sum_{i=1}^{C}\mathcal{T}_i(x)$, where $\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.

Dynamic algorithm configuration (DAC) is capable of generalizing over prior optimization approaches, as well as handling optimization of hyperparameters that need to be adjusted over multiple time-steps.

Image Source: [Biedenkapp et al.](http://ecai2020.eu/papers/1237_paper.pdf)

Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework

ResNeXt

Aggregated Residual Transformations for Deep Neural Networks

**Discriminative Adversarial Search**, or **DAS**, is a sequence decoding approach which aims to alleviate the effects of exposure bias and to optimize on the data distribution itself rather than for external metrics. Inspired by generative adversarial networks (GANs), wherein a discriminator is used to improve the generator, DAS differs from GANs in that the generator parameters are not updated at training time and the discriminator is only used to drive sequence generation at inference time.

Source	Aggregated Residual Transformations for Deep Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com