ityonemo / ityonemo/mavis_inference
backpropagation in sharded domain/range systems
- Dominant language
- Elixir
- Stars
- 1
- Forks
- 0
- PR merge metrics
- No merged PRs in 30d
Description
consider the a + b system, this has different outcomes. We could spec it as:
0, a -> a when a integer
a, 0 -> a when a integer
pos_integer, pos_integer -> pos_integer
neg_integer, neg_integer -> neg_integer
pos_integer, neg_integer -> integer
neg_integer, pos_integer -> integer
float, integer -> float
integer, float -> float
float, float -> float
how deep of a rabbit-hole should we go down?
Contributor guide
No contributing guide indexed for this repository
Research direction
Start by reviewing the proposed a + b outcome table and the existing inference-engine behavior for sharded domain/range systems. Clarify how deep backpropagation should go before choosing an implementation scope; done requires an agreed specification and corresponding behavior for the selected cases.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- elixir
- Domain
- compilers
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100