**Logistic Regression**, despite its name, is a linear model for classification rather than regression. Logistic regression is also known in the literature as logit regression, maximum-entropy classification (MaxEnt) or the log-linear classifier. In this model, the probabilities describing the possible outcomes of a single trial are modeled using a logistic function.

Source: [scikit-learn](https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression)

Image: [Michaelg2015](https://commons.wikimedia.org/wiki/User:Michaelg2015)

Fast-BAT is a new method for accelerated adversarial training.

Fast_BAT

Revisiting and Advancing Fast Adversarial Training Through The Lens of Bi-Level Optimization

Logistic Regression

**Rotary Position Embedding**, or **RoPE**, is a type of position embedding which encodes absolute positional information with rotation matrix and naturally incorporates explicit relative position dependency in self-attention formulation. Notably, RoPE comes with valuable properties such as flexibility of being expand to any sequence lengths, decaying inter-token dependency with increasing relative distances, and capability of equipping the linear self-attention with relative position encoding.

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