Row 13681
Content Data
This page contains data entry 13681 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.
Right that's exactly what they mean. Theyre not trying to save on computation, they're doing the opposite; they're using simpler models and making up for that fact by using bigger parametrizations in order to do *more* computation. They're literally trying to save on the complexity of the model implementation.
They say elsewhere in the paper that one of the reasons for this is that using equivariances in this context can require domain specific knowledge. According to them it's not enough just to make a model generally equivariant to e.g. translations and rotations; you also need to make specific parts of some molecules equivariant to rotations etc.
| Field | Value |
|---|---|
| text | Right that's exactly what they mean. Theyre not trying to save on computation, they're doing the opposite; they're using simpler models and making up for that fact by using bigger parametrizations in order to do *more* computation. They're literally trying to save on the complexity of the model implementation. They say elsewhere in the paper that one of the reasons for this is that using equivariances in this context can require domain specific knowledge. According to them it's not enough jus… |
| label | r/machinelearning |
| dataType | comment |
| communityName | r/MachineLearning |
| datetime | 2024-05-20 |
| username_encoded | Z0FBQUFBQm5Lakw3SHJIMGd2NkJRQkpYY2RFbHZUbHFCT0Z4cXBfWG9LSXVNVkpOV0hvZHhNd3BXa3dmdEhJMmZpM0JLMGVhSmVLekNmUUVGOFdiNEtIenlac1ZlWU5YYmc9PQ== |
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Raw Record
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"text": "Right that's exactly what they mean. Theyre not trying to save on computation, they're doing the opposite; they're using simpler models and making up for that fact by using bigger parametrizations in order to do *more* computation. They're literally trying to save on the complexity of the model implementation. \n\n\nThey say elsewhere in the paper that one of the reasons for this is that using equivariances in this context can require domain specific knowledge. According to them it's not enough just to make a model generally equivariant to e.g. translations and rotations; you also need to make specific parts of some molecules equivariant to rotations etc.",
"label": "r/machinelearning",
"dataType": "comment",
"communityName": "r/MachineLearning",
"datetime": "2024-05-20",
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}
Entry Information
- Entry ID: 13681
- Repository: Axioma AXP
- Dataset: arrmlet/reddit_dataset_36
- Total Entries: 100,000