The attention mechanism computes $$h_{i}^{\ell+1} = \text{Attention} \left( Q^{\ell} h_{i}^{\ell}, K^{\ell} h_{j}^{\ell}, V^{\ell} h_{j}^{\ell} \right)$$ for each node in the graph.
Here $f(x) = \sum_{i=0}^{n} \frac{a_i}{i!} x^i$ is the Taylor expansion and $g(x) = \int_0^1 e^{-t^2} dt$ is the Gaussian integral.
The loss function is defined as:
$$\mathcal{L}(\theta) = -\frac{1}{N} \sum_{i=1}^{N} \log p_\theta(x_i)$$
This costs $100 per unit, which is not math.
Let $x$ be a variable and $n$ be an integer.
The backslash delimiters work too: \(E = mc^2\) is inline and
\[F = ma\]
This $\alpha$ should not be touched inside pre tags.