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Convergent Learning: Do different neural networks learn the same representations?
Published on 2016-05-2710126 Views
Recent success in training deep neural networks have prompted active investigation into the features learned on their intermediate layers. Such research is difficult because it requires making sense o
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Presentation
Convergent Learning: Do different neural networks learn the same representations? - 100:00
Convergent Learning: Do different neural networks learn the same representations? - 200:09
“Grandmother neuron”00:18
Local or Distributed? - 101:10
Local or Distributed? - 201:44
Second-last layer: Distributed02:15
Middle layers: sometimes local02:41
Local or Distributed? - 302:56
Local or Distributed? - 403:34
Local or Distributed? - 504:24
Concepts05:27
Key technique - 106:16
Key technique - 207:11
Key technique - 307:31
Key technique - 407:43
Neuron Activation Statistics: Correlation07:58
Aside: is correlation a powerful enough measure?08:43
One-to-one alignment between features - 109:11
One-to-one alignment between features - 209:49
One-to-one alignment between features - 310:04
One-to-one alignment between features - 410:36
One-to-one alignment between features - 510:42
One-to-one alignment between features - 610:48
One-to-one alignment between features - 711:07
One-to-one alignment between features - 811:09
One-to-one alignment between features - 911:16
One-to-one alignment between features - 1011:19
One-to-one alignment between features - 1111:22
One-to-one alignment between features - 1211:49
One-to-one alignment between features - 1311:55
One-to-one alignment between features - 1412:14
One-to-one alignment between features - 1512:18
One-to-one alignment between features - 1612:24
One-to-one alignment between features - 1712:41
One-to-one alignment between features - 1812:53
One-to-one alignment between features - 1913:16
One-to-one alignment between features - 2013:25
One-to-one alignment between features - 2113:33
One-to-one alignment between features - 2213:35
One-to-one alignment between features - 2313:53
One-to-one alignment between features - 2413:56
One-to-one alignment between features - 2614:17
One-to-one alignment between features - 2714:26
One-to-one alignment between features - 2814:31
One-to-one alignment between features - 2914:44
One-to-one alignment between features - 3014:54
One-to-one alignment between features - 3114:56
Few-to-one alignment between features - 115:01
Few-to-one alignment between features - 215:40
Few-to-one alignment between features - 316:26
Few-to-one alignment between features - 416:39
Few-to-one alignment between features - 516:48
Few-to-one alignment between features - 616:50
Few-to-one alignment between features - 716:52
Finding the low-dimensional subspaces - 117:05
Finding the low-dimensional subspaces - 217:11
Finding the low-dimensional subspaces - 317:23
Finding the low-dimensional subspaces - 417:25
Finding the low-dimensional subspaces - 517:34
Conclusions - 117:44
Conclusions - 218:02
Conclusions - 318:18
Conclusions - 418:38
Conclusions - 518:43
Untitled18:58
Contact - 118:59
Contact - 219:04
Thanks19:08