BCI MI signal Classification Framework using Fuzzy integrals.

Paper: Ko, L. W., Lu, Y. C., Bustince, H., Chang, Y. C., Chang, Y., Ferandez, J., ... & Lin, C. T. (2019). Multimodal fuzzy fusion for enhancing the motor-imagery-based brain computer interface. IEEE Computational Intelligence Magazine, 14(1), 96-106.

Like [DARTS](https://paperswithcode.com/method/darts), except subtract the max weight gradients.

Max-W Weighting:
\begin{equation}
output_i = (1 - max(w) + w_i) * op_i(input_i)
\label{eqn:max_w}
\end{equation}

DARTS Max-W

sharpDARTS: Faster and More Accurate Differentiable Architecture Search

Choquet integral in decision analysis - lessons from the axiomatization

A **Dual Path Network** block is an image model block used in convolutional neural network. The idea of this module is to enable sharing of common features while maintaining the flexibility to explore new features through dual path architectures. In this sense it combines the benefits of [ResNets](https://paperswithcode.com/method/resnet) and [DenseNets](https://paperswithcode.com/method/densenet). It was proposed as part of the [DPN](https://paperswithcode.com/method/dpn) CNN architecture.

We formulate such a dual path architecture as follows:

$$x^{k} = \sum\limits\_{t=1}^{k-1} f\_t^{k}(h^t) \text{,}  $$

$$
y^{k} = \sum\limits\_{t=1}^{k-1} v\_t(h^t) = y^{k-1} + \phi^{k-1}(y^{k-1}) \text{,} \\\\
$$

$$
r^{k} = x^{k} + y^{k} \text{,} \\\\
$$

$$
h^k = g^k \left( r^{k} \right) \text{,}
$$

where $x^{k}$ and $y^{k}$ denote the extracted information at $k$-th step from individual path, $v_t(\cdot)$ is a feature learning function as $f_t^k(\cdot)$. The first equation refers to the densely connected path that enables exploring new features. The second equation refers to the residual path that enables common features re-usage. The third equation defines the dual path that integrates them and feeds them to the last transformation function in the last equation.

Source	Choquet integral in decision analysis - lessons from the axiomatization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com