Row 91907

Row ID: 91907 | Dataset Entry | Axioma AXP Content Repository

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This page contains data entry 91907 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

Hey all. With the recent Anthropic SAE paper making the rounds on Twitter, I was curious about interpretability of large visual or multimodal models ( for eg., SAM).

More specifically, if something similar to [Transformers circuits](https://transformer-circuits.pub/) have been implemented for ViTs.

If not, what's the reason for this gap?

FieldValue
text Hey all. With the recent Anthropic SAE paper making the rounds on Twitter, I was curious about interpretability of large visual or multimodal models ( for eg., SAM). More specifically, if something similar to [Transformers circuits](https://transformer-circuits.pub/) have been implemented for ViTs. If not, what's the reason for this gap?
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-25
username_encoded Z0FBQUFBQm5Lak1zV0NyU0dJblFfRkR6TkY1cXNvR1BKZUwxcmFKcU4xQS16UmI5Y3ZLNWNKblZOYXlvZF8tOEVfRmh5V0JmZWJkc2x3cVE4dlptaHdfdjV0SUlVY25vbFBMaDFLMjdOQTNxaklfbk9Vejl6STA9
url_encoded Z0FBQUFBQm5Lak8tSDFGRTBudl9pV05tZ0NrTVVDUDE1TmtuS0pVcVZpVzVMbUFmWW5aNXZvRFZnT1dlSXZtdmlwbHVRbURMakFYNzRybUthUXZaS19OY1g0ZUlHUkRRbUkzTFZXU1B0OGNVbl9EZi1odDJHWXhibTA3a3NhS1pNUlF2cE50dks2OVpkZW9tQXdleHRnbkRnb0lyejZwWGhJZjhEQjl2cS1rYUNCaWVnUTd4dEdha0JULVVZeEdmbHlkR2dPNmNTOXcz

Raw Record

{
  "text": "Hey all. With the recent Anthropic SAE paper making the rounds on Twitter, I was curious about interpretability of large visual or multimodal models ( for eg., SAM). \n\nMore specifically, if something similar to [Transformers circuits](https://transformer-circuits.pub/) have been implemented for ViTs. \n\nIf not, what's the reason for this gap?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-25",
  "username_encoded": "Z0FBQUFBQm5Lak1zV0NyU0dJblFfRkR6TkY1cXNvR1BKZUwxcmFKcU4xQS16UmI5Y3ZLNWNKblZOYXlvZF8tOEVfRmh5V0JmZWJkc2x3cVE4dlptaHdfdjV0SUlVY25vbFBMaDFLMjdOQTNxaklfbk9Vejl6STA9",
  "url_encoded": "Z0FBQUFBQm5Lak8tSDFGRTBudl9pV05tZ0NrTVVDUDE1TmtuS0pVcVZpVzVMbUFmWW5aNXZvRFZnT1dlSXZtdmlwbHVRbURMakFYNzRybUthUXZaS19OY1g0ZUlHUkRRbUkzTFZXU1B0OGNVbl9EZi1odDJHWXhibTA3a3NhS1pNUlF2cE50dks2OVpkZW9tQXdleHRnbkRnb0lyejZwWGhJZjhEQjl2cS1rYUNCaWVnUTd4dEdha0JULVVZeEdmbHlkR2dPNmNTOXcz"
}
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