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)

Mirror-BERT converts pretrained language models into effective universal text encoders without any supervision, in 20-30 seconds. It is an extremely simple, fast, and effective contrastive learning technique. It relies on fully identical *or* slightly modified string pairs as positive (i.e., synonymous) fine-tuning examples, and aims to maximise their similarity during identity fine-tuning.

Mirror-BERT

Fast, Effective, and Self-Supervised: Transforming Masked Language Models into Universal Lexical and Sentence Encoders

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

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.

Source	Dynamic Algorithm Configuration: Foundation of a New Meta-Algorithmic Framework
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com