In a machine learning paper I'm reading, I came across the following notation:
In our experiments we parameterize these potential functions as an exponential of a linear scoring function
$\Phi(a_{i-1},a_i,\mathbf{x},\mathbf{y}) = \operatorname{exp}\Lambda\cdot\mathbf{f}(a_{i-1},a_i,\mathbf{x},\mathbf{y})$
where $\mathbf{f}$ is a vector of feature functions...
What does the "$\operatorname{exp}\Lambda\cdot$" notation mean?
Dot product of vectors. If $\Lambda = (\lambda_1,\lambda_2,\dots,\lambda_n)$ and the components of $\mathbf f$ are $f_1,f_2,\dots, f_n$, then the dot product is $$ \Lambda\cdot\mathbf{f}(a_{i-1},a_i,\mathbf{x},\mathbf{y}) = \lambda_1 f_1(a_{i-1},a_i,\mathbf{x},\mathbf{y})+ \lambda_1 f_1(a_{i-1},a_i,\mathbf{x},\mathbf{y})+\dots+ \lambda_n f_n(a_{i-1},a_i,\mathbf{x},\mathbf{y}) $$ which is what they called a "linear scoring function". So $\mathbf f$ is a "vector of feature functions", and $f_1, f_2, \dots, f_n$ are the "feature functions". I used $n$ for the number of features.
Then you take the exponential of that.
