You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
Below is an example of the two-stage experiment scheme.
29
-
At the first stage, regularizer with parameter <imgsrc="https://render.githubusercontent.com/render/math?math=\tau"> taking values in some range $\{\tau_1, \tau_2, \tau_3\}$ is applied.
30
-
Best models after the first stage are \emph{Model 1} and \emph{Model 2}~---~so \emph{Model 3} is not taking part in the training process anymore.
31
-
The second stage is connected with another regularizer with parameter $\xi$ taking values in range $\{\xi_1, \xi_2\}$.
32
-
As a result of this stage, two descendant models of \emph{Model 1} and two descendant models of \emph{Model 2} are obtained.
At the first stage, regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\tau"> taking values in some range <img src="https://render.githubusercontent.com/render/math?math=\{\tau_1, \tau_2, \tau_3\}"> is applied.
31
+
Best models after the first stage are <em>Model 1</em> and <em>Model 2</em>~---~so <em>Model 3</em> is not taking part in the training process anymore.
32
+
The second stage is connected with another regularizer with parameter <img src="https://render.githubusercontent.com/render/math?math=\xi"> taking values in range <img src="https://render.githubusercontent.com/render/math?math=\{\xi_1, \xi_2\}">.
33
+
As a result of this stage, two descendant models of <em>Model 1</em> and two descendant models of <em>Model 2</em> are obtained.
0 commit comments