Row 49600

Row ID: 49600 | Dataset Entry | Axioma AXP Content Repository

Content Data

This page contains data entry 49600 from the Axioma AXP content repository. The structured data below represents the complete record for this entry.

A while ago I read a paper that does a comparison between a convolutional model, a locally connected one (same as cnn but without parameter sharing, so each image location has a different kernel), and a densely connected model. Specifically, they make each of these models have the same number of activations, where most comparisons keep parameters constant. This was a cool paper because it places each model on an even playing field in terms of what it can express and this shows how the benefits of a cnn can be decomposed into locality and parameter sharing.

I'm struggling to find this paper however. Does anyone have any recommendations?

FieldValue
text A while ago I read a paper that does a comparison between a convolutional model, a locally connected one (same as cnn but without parameter sharing, so each image location has a different kernel), and a densely connected model. Specifically, they make each of these models have the same number of activations, where most comparisons keep parameters constant. This was a cool paper because it places each model on an even playing field in terms of what it can express and this shows how the benefits o…
label r/machinelearning
dataType post
communityName r/MachineLearning
datetime 2024-05-22
username_encoded Z0FBQUFBQm5Lak1TSktZYWlFWHNsTHlweUtRRlpYWDdJcjFSbE9xdXU3OTFyWkFwam5fLXF4VlF5STdULXlQZVFMcjd1U1B5eVJjZU4wMzZzT0dRNi1RcmZuNzk0cGdEUlE9PQ==
url_encoded Z0FBQUFBQm5Lak9oM0hGbHRQOXFQWG16MVBtRUZVLXYwdEw5VTZPMGRqa2R0ejZka2ItNlo0ZHVjUzJvekItOUtxeUJlMWZubFktWllmdFRmbnc2eFdvdWI5YXNQWndERkNRWmMyVE1mSzdKRi0tQkRGTi1mc2Mxa2xId2VXZHUwQ096RzhYbHdwQUhLQndsQjl6dVlTcEl6bkFZYUpmY015di1hMmFtZ2Zkb1lnbWNhLTljR2VTcVRrOFNmX1c3Tkx2Ny1hQVdUZnNmYlFMQUVFQTVxMTFPNnd5YUJZVzRzQT09

Raw Record

{
  "text": "A while ago I read a paper that does a comparison between a convolutional model, a locally connected one (same as cnn but without parameter sharing, so each image location has a different kernel), and a densely connected model. Specifically, they make each of these models have the same number of activations, where most comparisons keep parameters constant. This was a cool paper because it places each model on an even playing field in terms of what it can express and this shows how the benefits of a cnn can be decomposed into locality and parameter sharing. \n\nI'm struggling to find this paper however. Does anyone have any recommendations?",
  "label": "r/machinelearning",
  "dataType": "post",
  "communityName": "r/MachineLearning",
  "datetime": "2024-05-22",
  "username_encoded": "Z0FBQUFBQm5Lak1TSktZYWlFWHNsTHlweUtRRlpYWDdJcjFSbE9xdXU3OTFyWkFwam5fLXF4VlF5STdULXlQZVFMcjd1U1B5eVJjZU4wMzZzT0dRNi1RcmZuNzk0cGdEUlE9PQ==",
  "url_encoded": "Z0FBQUFBQm5Lak9oM0hGbHRQOXFQWG16MVBtRUZVLXYwdEw5VTZPMGRqa2R0ejZka2ItNlo0ZHVjUzJvekItOUtxeUJlMWZubFktWllmdFRmbnc2eFdvdWI5YXNQWndERkNRWmMyVE1mSzdKRi0tQkRGTi1mc2Mxa2xId2VXZHUwQ096RzhYbHdwQUhLQndsQjl6dVlTcEl6bkFZYUpmY015di1hMmFtZ2Zkb1lnbWNhLTljR2VTcVRrOFNmX1c3Tkx2Ny1hQVdUZnNmYlFMQUVFQTVxMTFPNnd5YUJZVzRzQT09"
}
Explore Dataset Explore Row

Entry Information