| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T70 |
0-127 |
Sentence |
denotes |
As is often the case with training neural networks, the problem is rooted in gathering large amounts of data on which to train. |
| T71 |
128-262 |
Sentence |
denotes |
For this, we make use of the AnyLogic parallelized computation ability, to simulate large batches of simulation runs at the same time. |
| T72 |
263-397 |
Sentence |
denotes |
In doing so, we strategically sample across a large range of parameters, near the domain of interest required of real-world scenarios. |
| T73 |
398-450 |
Sentence |
denotes |
Parameter simulation ranges can be found in Table 2. |
