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16 December 2015
- 17:1317:13, 16 December 2015 diff hist +2 deep Sparse Rectifier Neural Networks →Potential problems of rectified linear units
- 17:1317:13, 16 December 2015 diff hist +748 deep Sparse Rectifier Neural Networks →Potential problems of rectified linear units
- 16:0516:05, 16 December 2015 diff hist +425 scene Parsing with Multiscale Feature Learning, Purity Trees, and Optimal Covers Machines →Pre-processing
14 December 2015
- 16:3416:34, 14 December 2015 diff hist +479 extracting and Composing Robust Features with Denoising Autoencoders →Analysis of the Denoising Autoencoder
- 15:2415:24, 14 December 2015 diff hist 0 distributed Representations of Words and Phrases and their Compositionality →Other techniques for sentence representation
- 15:2415:24, 14 December 2015 diff hist 0 distributed Representations of Words and Phrases and their Compositionality →Other techniques for sentence representation
- 15:2315:23, 14 December 2015 diff hist 0 File:Recur-auto.png uploaded a new version of "File:Recur-auto.png" current
- 15:2215:22, 14 December 2015 diff hist 0 distributed Representations of Words and Phrases and their Compositionality →Other techniques for sentence representation
- 15:2215:22, 14 December 2015 diff hist +42 distributed Representations of Words and Phrases and their Compositionality No edit summary
- 15:2015:20, 14 December 2015 diff hist 0 N File:Recur-auto.png No edit summary
- 15:1915:19, 14 December 2015 diff hist +107 distributed Representations of Words and Phrases and their Compositionality No edit summary
- 15:1715:17, 14 December 2015 diff hist +974 distributed Representations of Words and Phrases and their Compositionality No edit summary
12 December 2015
- 19:1519:15, 12 December 2015 diff hist +1,084 learning Phrase Representations →Alternative Models
30 November 2015
- 18:1318:13, 30 November 2015 diff hist +704 on the difficulty of training recurrent neural networks →The Mechanics
- 17:5717:57, 30 November 2015 diff hist +644 on the difficulty of training recurrent neural networks →The Mechanics
- 17:4017:40, 30 November 2015 diff hist +101 on the difficulty of training recurrent neural networks No edit summary
- 17:3417:34, 30 November 2015 diff hist +22 on the difficulty of training recurrent neural networks No edit summary
16 November 2015
- 22:1922:19, 16 November 2015 diff hist +21 deep Learning of the tissue-regulated splicing code →Model
- 22:1822:18, 16 November 2015 diff hist −2 deep Learning of the tissue-regulated splicing code →Model
- 22:1822:18, 16 November 2015 diff hist +250 deep Learning of the tissue-regulated splicing code No edit summary
- 22:1122:11, 16 November 2015 diff hist 0 N File:Modell.png No edit summary current
- 22:0922:09, 16 November 2015 diff hist 0 N File:LMH.png No edit summary current
- 22:0922:09, 16 November 2015 diff hist 0 N File:DNI.png No edit summary current
- 22:0922:09, 16 November 2015 diff hist +376 deep Learning of the tissue-regulated splicing code No edit summary
- 21:5721:57, 16 November 2015 diff hist +1,879 deep Learning of the tissue-regulated splicing code No edit summary
- 21:0921:09, 16 November 2015 diff hist +6 deep Learning of the tissue-regulated splicing code →Model
- 17:4817:48, 16 November 2015 diff hist +107 deep Learning of the tissue-regulated splicing code →Model
- 17:0717:07, 16 November 2015 diff hist +1,766 deep Learning of the tissue-regulated splicing code No edit summary
- 11:4811:48, 16 November 2015 diff hist 0 deep Learning of the tissue-regulated splicing code →Introduction
- 11:4811:48, 16 November 2015 diff hist +2 deep Learning of the tissue-regulated splicing code →Introduction
- 11:4711:47, 16 November 2015 diff hist +1,148 N deep Learning of the tissue-regulated splicing code Created page with "= Introduction = Alternative splicing(AS) is a regulated process during gene expression that enables the same gene to give rise to splicing isoforms containing different combina..."
12 November 2015
- 21:5321:53, 12 November 2015 diff hist +28 dropout No edit summary
- 21:5121:51, 12 November 2015 diff hist +67 dropout →Introduction
- 21:4721:47, 12 November 2015 diff hist +707 dropout →Model
- 21:3421:34, 12 November 2015 diff hist −4 dropout No edit summary
- 21:3321:33, 12 November 2015 diff hist +12 dropout →Result
- 21:3321:33, 12 November 2015 diff hist 0 dropout →Comparison
- 21:3221:32, 12 November 2015 diff hist 0 N File:Result.png No edit summary
- 21:3021:30, 12 November 2015 diff hist +13 dropout →Comparison
- 21:2821:28, 12 November 2015 diff hist +692 dropout No edit summary
- 21:1421:14, 12 November 2015 diff hist +218 dropout No edit summary
- 21:1021:10, 12 November 2015 diff hist +28 dropout →Effects of Dropout
- 21:0921:09, 12 November 2015 diff hist +16 dropout →Model
- 21:0821:08, 12 November 2015 diff hist 0 N File:Test.png No edit summary current
- 21:0821:08, 12 November 2015 diff hist 0 N File:Sparsity.png No edit summary current
- 21:0721:07, 12 November 2015 diff hist 0 N File:Pvalue.png No edit summary current
- 21:0721:07, 12 November 2015 diff hist 0 N File:Comparison.png No edit summary current
- 21:0721:07, 12 November 2015 diff hist 0 N File:Feature.png No edit summary current
- 21:0621:06, 12 November 2015 diff hist 0 N File:Datasize.png No edit summary current
- 21:0621:06, 12 November 2015 diff hist −1 dropout No edit summary
- 21:0221:02, 12 November 2015 diff hist 0 N File:Intro.png No edit summary current
- 20:3220:32, 12 November 2015 diff hist +2,781 dropout No edit summary
9 November 2015
- 00:5400:54, 9 November 2015 diff hist 0 f15Stat946PaperSignUp →Set A
- 00:5300:53, 9 November 2015 diff hist +222 f15Stat946PaperSignUp →Set B
- 00:1600:16, 9 November 2015 diff hist +1 dropout No edit summary
- 00:1600:16, 9 November 2015 diff hist +77 dropout No edit summary
2 November 2015
- 23:1623:16, 2 November 2015 diff hist +1,038 dropout →Model
- 22:3722:37, 2 November 2015 diff hist +452 dropout No edit summary
- 22:2022:20, 2 November 2015 diff hist +764 N dropout Created page with "= Introduction = Dropout is one of the techniques for preventing overfitting in deep neural network which contains a large number of parameters. The key idea is to randomly drop ..."
- 22:1822:18, 2 November 2015 diff hist +22 f15Stat946PaperSignUp No edit summary
- 20:5720:57, 2 November 2015 diff hist +1,767 learning Hierarchical Features for Scene Labeling No edit summary
28 October 2015
- 11:5611:56, 28 October 2015 diff hist +614 goingDeeperWithConvolutions No edit summary
22 October 2015
- 00:0400:04, 22 October 2015 diff hist +4 parsing natural scenes and natural language with recursive neural networks →Unsupervised Recursive Autoencoer for structure Predictionhttp://nlp.stanford.edu/pubs/SocherPenningtonHuangNgManning_EMNLP2011.pdf.
- 00:0100:01, 22 October 2015 diff hist +1,674 parsing natural scenes and natural language with recursive neural networks No edit summary
21 October 2015
- 11:2611:26, 21 October 2015 diff hist 0 stat946f15/Sequence to sequence learning with neural networks →More on Recurrent Neural Network
- 11:2611:26, 21 October 2015 diff hist +1 stat946f15/Sequence to sequence learning with neural networks →More on Recurrent Neural Network
- 11:2511:25, 21 October 2015 diff hist +630 stat946f15/Sequence to sequence learning with neural networks →More on Recurrent Neural Network
- 11:1211:12, 21 October 2015 diff hist +717 stat946f15/Sequence to sequence learning with neural networks No edit summary
5 October 2015
- 10:5810:58, 5 October 2015 diff hist +6 f15Stat946PaperSignUp No edit summary
- 10:5810:58, 5 October 2015 diff hist +136 f15Stat946PaperSignUp No edit summary