**MobileDet** is an object detection model developed for mobile accelerators. MobileDets uses regular convolutions extensively on EdgeTPUs and DSPs, especially in the early stage of the network where depthwise convolutions tend to be less efficient.  This helps boost the latency-accuracy trade-off for object detection on accelerators, provided that they are placed strategically in the network via [neural architecture search](https://paperswithcode.com/method/neural-architecture-search). By incorporating regular convolutions in the search space and directly optimizing the network architectures for object detection, an efficient family of object detection models is obtained.

TabNN is a universal neural network solution to derive effective NN architectures for tabular data in all kinds of tasks automatically. Specifically, the design of TabNN follows two principles: to explicitly leverage expressive feature combinations and to reduce model complexity. Since GBDT has empirically proven its strength in modeling tabular data, GBDT is used to power the implementation of TabNN.

TabNN

TabNN: A Universal Neural Network Solution for Tabular Data

MobileDet

MobileDets: Searching for Object Detection Architectures for Mobile Accelerators

**AutoInt** is a deep tabular learning method that models high-order feature interactions of input features. AutoInt can be applied to both numerical and categorical input features. Specifically, both the numerical and categorical features are mapped into the same low-dimensional space. Afterwards, a multi-head self-attentive neural network with residual connections is proposed to explicitly model the feature interactions in the low-dimensional space. With different layers of the multi-head self-attentive neural networks, different orders of feature combinations of input features can be modeled.

Source	MobileDets: Searching for Object Detection Architectures for Mobile Accelerators
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com