User contributions for S362khan
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6 December 2018
- 11:2411:24, 6 December 2018 diff hist +41 DeepVO Towards end to end visual odometry with deep RNN →Training and Optimization
- 11:1311:13, 6 December 2018 diff hist +59 m DeepVO Towards end to end visual odometry with deep RNN →Related Work
- 11:1111:11, 6 December 2018 diff hist −16 m DeepVO Towards end to end visual odometry with deep RNN →Related Work
- 11:0911:09, 6 December 2018 diff hist −1 m DeepVO Towards end to end visual odometry with deep RNN →Related Work
5 December 2018
- 23:5723:57, 5 December 2018 diff hist −12 Wasserstein Auto-encoders →Original Contributions
- 23:4623:46, 5 December 2018 diff hist +7 m Wasserstein Auto-encoders →Original Contributions
- 23:3923:39, 5 December 2018 diff hist −12 m Wasserstein Auto-encoders →Introduction
- 23:2023:20, 5 December 2018 diff hist +195 m MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION →Background
- 23:1523:15, 5 December 2018 diff hist +95 m MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION →Background
- 23:0823:08, 5 December 2018 diff hist +30 m MULTI-VIEW DATA GENERATION WITHOUT VIEW SUPERVISION →Contributions
- 22:0522:05, 5 December 2018 diff hist −17 m Fix your classifier: the marginal value of training the last weight layer →Shortcomings of the Final Classification Layer and its Solution
- 21:4721:47, 5 December 2018 diff hist +7 m Fix your classifier: the marginal value of training the last weight layer →Using a Fixed Classifier
- 21:1721:17, 5 December 2018 diff hist −8 Fix your classifier: the marginal value of training the last weight layer →Using a Fixed Classifier
- 21:0621:06, 5 December 2018 diff hist −37 Fix your classifier: the marginal value of training the last weight layer →Shortcomings of the Final Classification Layer and its Solution
- 20:4520:45, 5 December 2018 diff hist −1 m Fix your classifier: the marginal value of training the last weight layer →Background
- 20:4520:45, 5 December 2018 diff hist −42 Fix your classifier: the marginal value of training the last weight layer →Background
- 20:3520:35, 5 December 2018 diff hist −26 Fix your classifier: the marginal value of training the last weight layer →Previous Work
- 20:3020:30, 5 December 2018 diff hist +56 Fix your classifier: the marginal value of training the last weight layer →Introduction
4 December 2018
- 11:1211:12, 4 December 2018 diff hist −70 DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE →THE EFFECTIVE LEARNING RATE AND THE ACCUMULATION VARIABLE
- 10:4910:49, 4 December 2018 diff hist −648 DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE →SIMULATED ANNEALING AND THE GENERALIZATION GAP: - Remove multiple explanation of simulated annealing.
- 10:2910:29, 4 December 2018 diff hist +84 m DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE →INTRODUCTION
- 10:2110:21, 4 December 2018 diff hist −1 DON'T DECAY THE LEARNING RATE , INCREASE THE BATCH SIZE →INTRODUCTION
29 November 2018
- 15:5615:56, 29 November 2018 diff hist +596 ShakeDrop Regularization →Existing Methods
- 11:3911:39, 29 November 2018 diff hist +74 ShakeDrop Regularization →Existing Methods
28 November 2018
- 11:0711:07, 28 November 2018 diff hist +90 stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series →Model Architecture
27 November 2018
- 12:1112:11, 27 November 2018 diff hist +29 stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series →Introduction
- 12:0712:07, 27 November 2018 diff hist −2 m stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series →Introduction
- 12:0112:01, 27 November 2018 diff hist +22 m stat946F18/Autoregressive Convolutional Neural Networks for Asynchronous Time Series →Introduction
- 11:2611:26, 27 November 2018 diff hist −51 Visual Reinforcement Learning with Imagined Goals →Goal-Conditioned Reinforcement Learning
- 11:1611:16, 27 November 2018 diff hist +234 Visual Reinforcement Learning with Imagined Goals →Goal-Conditioned Reinforcement Learning
- 11:0311:03, 27 November 2018 diff hist −4 m Visual Reinforcement Learning with Imagined Goals →Introduction and Motivation
- 10:5810:58, 27 November 2018 diff hist +7 m Visual Reinforcement Learning with Imagined Goals →Introduction and Motivation: Center Image
25 November 2018
- 20:4620:46, 25 November 2018 diff hist +152 Unsupervised Neural Machine Translation →Denoising
- 20:4420:44, 25 November 2018 diff hist −100 Unsupervised Neural Machine Translation →Methodology
- 20:1720:17, 25 November 2018 diff hist +89 Unsupervised Neural Machine Translation →Low-Resource Neural Machine Translation
- 20:0320:03, 25 November 2018 diff hist −156 Unsupervised Neural Machine Translation →Statistical Decipherment for Machine Translation
- 19:4319:43, 25 November 2018 diff hist −72 Unsupervised Neural Machine Translation →Introduction
23 November 2018
- 23:0023:00, 23 November 2018 diff hist −80 a neural representation of sketch drawings →Sketch-RNN
- 22:1322:13, 23 November 2018 diff hist +40 a neural representation of sketch drawings →Sketch-RNN
- 22:1222:12, 23 November 2018 diff hist +236 a neural representation of sketch drawings →Sketch-RNN
21 November 2018
- 17:4717:47, 21 November 2018 diff hist +81 conditional neural process →Other Sources
- 12:5912:59, 21 November 2018 diff hist +239 conditional neural process →Conclusion
- 12:3412:34, 21 November 2018 diff hist +1 m conditional neural process →Conditional Neural Process
- 12:3312:33, 21 November 2018 diff hist +1 m conditional neural process →Conditional Neural Process
- 12:3212:32, 21 November 2018 diff hist +2 m conditional neural process Latex fix
- 12:3112:31, 21 November 2018 diff hist +1 conditional neural process →Model: Latex Fix
19 November 2018
- 21:3621:36, 19 November 2018 diff hist +104 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Conclusion
- 21:2921:29, 19 November 2018 diff hist +2 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Pixel Defend, [Song, 2018]
- 21:2821:28, 19 November 2018 diff hist +135 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Stochastic Gradients
- 21:2521:25, 19 November 2018 diff hist −31 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Input Transformation, [Guo, 2018]