High-dimensional input and why exhaustive testing fails
The introduction says neural networks can handle data with many dimensions, naming perception and connected-device data, and that the space of possible inputs is variable and too large to cover exhaustively. Sampling is the answer to that condition.
The requirement in plain English
The introduction says neural networks can handle data with many dimensions, naming perception and connected-device data, and that the space of possible inputs is variable and too large to cover exhaustively. Sampling is the answer to that condition.
What the public record says
The public text names high dimensionality, variability and the impracticality of exhaustive coverage, then says sampling lets a team see far more of the input space.
Why it matters to you
This paragraph is the justification a sampling plan needs. A reviewer will ask why you did not test every input; the answer is that the space is too large and too variable, and the document says so. The paragraph also sets the terms of the defence: the sample must be wide, and the frame must be described. A sample of a thousand images from one camera is not a wide exploration of a perception space, even if the count looks large.
Evidence a reviewer asks for
- A description of the input space: dimensions, ranges, sources and known shifts.
- The sampling frame and how it was drawn, with the reason each stratum was included.
- A note on what the sample cannot cover.
- The reviewer's sign-off on the frame.
Checks to run this month
- Can you describe the input space in numbers, not adjectives?
- Does the sample cross the sources and conditions that matter?
- Is the limit of the frame stated in the result?
Dates
FDIS registered 2026-08-21.
Cross-references
What this page does not state
No sample size, stratification rule or coverage measure is stated.