Row 7518

Row ID: 7518 | Dataset Entry | Axioma AXP Content Repository

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Trying to familiarise myself with the mamba architecture, hence familiarising myself with SSMs, hence familiarising myself with Linear RNNs. I have looked over resources on SSMs, S4 and Mamba but I’m unable to find an explanation. on why Linear RNNs with SSM parameterization improves performance. I can’t wrap my head around it intuitively either - why are linear transformations sufficient for seq2seq tasks?

Are there any exhaustive mathematical explanations, or even videos on how linear RNNs can outperform transformers on certain tasks?

FieldValue
text Trying to familiarise myself with the mamba architecture, hence familiarising myself with SSMs, hence familiarising myself with Linear RNNs. I have looked over resources on SSMs, S4 and Mamba but I’m unable to find an explanation. on why Linear RNNs with SSM parameterization improves performance. I can’t wrap my head around it intuitively either - why are linear transformations sufficient for seq2seq tasks? Are there any exhaustive mathematical explanations, or even videos on how linear RNNs ca…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-16
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Raw Record

{
  "text": "Trying to familiarise myself with the mamba architecture, hence familiarising myself with SSMs, hence familiarising myself with Linear RNNs. I have looked over resources on SSMs, S4 and Mamba but I’m unable to find an explanation. on why Linear RNNs with SSM parameterization improves performance. I can’t wrap my head around it intuitively either - why are linear transformations sufficient for seq2seq tasks?\n\nAre there any exhaustive mathematical explanations, or even videos on how linear RNNs can outperform transformers on certain tasks?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-16",
  "username_encoded": "Z0FBQUFBQm5LakwzOVZmVjU0djlWLW1JTWZMNmNYbDVrU3Q0TmtDZjUzQ1BDRVhkTV8wZ2VDY2NnWWp0bWdHTnlNM193Q2R1dVlvVXpSOTZoUVJSNE9lQnE5Q1BqVWtzQVE9PQ==",
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}
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