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.

The **Feature-wise linear modulation** (**FiLM**) module combines information from both noisy waveform and input mel-spectrogram. It is used in the [WaveGrad](https://paperswithcode.com/method/wavegrad) model. The authors also added iteration index $n$ which indicates the noise level of the input waveform by using the [Transformer](https://paperswithcode.com/method/transformer) sinusoidal positional embedding. To condition on the noise level directly, $n$ is replaced by $\sqrt{\bar{\alpha}}$ and a linear scale $C = 5000$ is applied. The FiLM module produces both scale and bias vectors given inputs, which are used in a UBlock for feature-wise affine transformation as:

$$ \gamma\left(D, \sqrt{\bar{\alpha}}\right) \odot U + \zeta\left(D, \sqrt{\bar{\alpha}}\right) $$

where $\gamma$ and $\zeta$ correspond to the scaling and shift vectors from the FiLM module, $D$ is the output from corresponding [DBlock](https://paperswithcode.com/method/dblock), $U$ is an intermediate output in the UBlock.

FiLM Module

WaveGrad: Estimating Gradients for Waveform Generation

TabNN

TabNN: A Universal Neural Network Solution for Tabular Data

**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.

Source	TabNN: A Universal Neural Network Solution for Tabular Data
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com