**Neo-fuzzy-neuron** is a type of artificial neural network that combines the characteristics of both fuzzy logic and neural networks. It uses a fuzzy inference system to model non-linear relationships between inputs and outputs, and a feedforward neural network to learn the parameters of the fuzzy system. The combination of these two approaches provides a flexible and powerful tool for solving a wide range of problems in areas such as pattern recognition, control, and prediction.

**CornerNet-Squeeze** is an object detector that extends [CornerNet](https://paperswithcode.com/method/cornernet) with a new compact hourglass architecture that makes use of fire modules with depthwise separable convolutions.

CornerNet-Squeeze

CornerNet-Lite: Efficient Keypoint Based Object Detection

Neo-fuzzy-neuron based new approach to system modeling, with application to actual system

**Flan-T5** is the instruction fine-tuned version of **T5** or **Text-to-Text Transfer Transformer** Language Model.

Source	Neo-fuzzy-neuron based new approach to system modeling, with application to actual system
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com