Row 10217

Row ID: 10217 | Dataset Entry | Axioma AXP Content Repository

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Yeah this idea of rotating data is called data augmentation and it’s garbage in comparison to actually having a network that has invariant or equivariant learnable features.

The research on symmetry problems in ML is already quite mature.

Btw just because a model is invariant doesn’t mean it’s good. You can construct invariant models by only using distances between points, but then you can’t differentiate 3-body symmetries. The SOTA models have 5-6 body learnable representations built into them.

Also not saying that any of this might be important for your specific problem since it seems you’re doing inverse design, but I think it’s interesting for you to learn about since it could be helpful.

FieldValue
text Yeah this idea of rotating data is called data augmentation and it’s garbage in comparison to actually having a network that has invariant or equivariant learnable features. The research on symmetry problems in ML is already quite mature. Btw just because a model is invariant doesn’t mean it’s good. You can construct invariant models by only using distances between points, but then you can’t differentiate 3-body symmetries. The SOTA models have 5-6 body learnable representations built into th…
label r/machinelearning
dataType comment
communityName r/MachineLearning
datetime 2024-05-20
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{
  "text": "Yeah this idea of rotating data is called data augmentation and it’s garbage in comparison to actually having a network that has invariant or equivariant learnable features. \n\nThe research on symmetry problems in ML is already quite mature.\n\nBtw just because a model is invariant doesn’t mean it’s good. You can construct invariant models by only using distances between points, but then you can’t differentiate 3-body symmetries. The SOTA models have 5-6 body learnable representations built into them.\n\nAlso not saying that any of this might be important for your specific problem since it seems you’re doing inverse design, but I think it’s interesting for you to learn about since it could be helpful.",
  "label": "r/machinelearning",
  "dataType": "comment",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-20",
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