**LightAutoML** is an AutoML solution targeted for financial services companies. A typical LightAutoML pipeline scheme is presented in the Figure, each pipeline containing:

- Reader: object that receives raw data and task as input, calculates some useful metadata, performs initial data cleaning and decides about data manipulations that should be done before fitting different model types.

- LightAutoML inner datasets that contains metadata and CV iterators that implements validation scheme for the datasets.

- Multiple ML Pipelines that are stacked and/or blended to get a single prediction.

An ML pipeline in LightAutoML is one or multiple ML models that share a single data preprocessing and validation scheme. The preprocessing step may have up to two feature selection steps, a feature engineering step or even just be empty if no preprocessing is needed. The ML pipelines can be computed independently on the same datasets and then blended together using averaging (or weighted averaging). Alternatively, a stacking ensemble scheme can be used to build multi level ensemble architectures.

**Cascade Corner Pooling** is a pooling layer for object detection that builds upon the [corner pooling](https://paperswithcode.com/method/corner-pooling) operation. Corners are often outside the objects, which lacks local appearance features. [CornerNet](https://paperswithcode.com/method/cornernet) uses corner pooling to address this issue, where we find the maximum values on the boundary directions so as to determine corners. However, it makes corners sensitive to the edges. To address this problem, we need to let corners see the visual patterns of objects. Cascade corner pooling first looks along a boundary to find a boundary maximum value, then looks inside along the location of the boundary maximum value to find an internal maximum value, and finally, add the two maximum values together. By doing this, the corners obtain both the the boundary information and the visual patterns of objects.

Cascade Corner Pooling

CenterNet: Keypoint Triplets for Object Detection

LightAutoML

LightAutoML: AutoML Solution for a Large Financial Services Ecosystem

**YOLOv4** is a one-stage object detection model that improves on [YOLOv3](https://paperswithcode.com/method/yolov3) with several bags of tricks and modules introduced in the literature. The components section below details the tricks and modules used.

Source	LightAutoML: AutoML Solution for a Large Financial Services Ecosystem
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com