**Linear discriminant analysis** (LDA), normal discriminant analysis (NDA), or discriminant function analysis is a generalization of Fisher's linear discriminant, a method used in statistics, pattern recognition, and machine learning to find a linear combination of features that characterizes or separates two or more classes of objects or events. The resulting combination may be used as a linear classifier, or, more commonly, for dimensionality reduction before later classification.

Extracted from [Wikipedia](https://en.wikipedia.org/wiki/Linear_discriminant_analysis)

**Source**:

Paper: [Linear Discriminant Analysis: A Detailed Tutorial](https://dx.doi.org/10.3233/AIC-170729)

Public version: [Linear Discriminant Analysis: A Detailed Tutorial](https://usir.salford.ac.uk/id/eprint/52074/)

**RPDet**, or **RepPoints Detector**, is a anchor-free, two-stage object detection model based on deformable convolutions.  [RepPoints](https://paperswithcode.com/method/reppoints) serve as the basic object representation throughout the detection system. Starting from the center points, the first set of RepPoints is obtained via regressing offsets over the center points. The learning of these RepPoints is driven by two objectives: 1) the top-left and bottom-right points distance loss between the induced pseudo box and the ground-truth bounding box; 2) the object recognition loss of the subsequent stage.

RPDet

RepPoints: Point Set Representation for Object Detection

SimVLM is a minimalist pretraining framework to reduce training complexity by exploiting large-scale weak supervision. It is trained end-to-end with a single prefix language modeling (PrefixLM) objective. PrefixLM enables bidirectional attention within the prefix sequence, and thus it is applicable for both decoder-only
and encoder-decoder sequence-to-sequence language models.

Year	2000
Data Source	Papers with Code archive (CC BY-SA)