User contributions for S362khan
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6 December 2018
- 21:5721:57, 6 December 2018 diff hist +6 m Pixels to Graphs by Associative Embedding →The Architecture:
- 21:2121:21, 6 December 2018 diff hist −1 m Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction →Background, Notations, and Definitions current
- 21:1721:17, 6 December 2018 diff hist +56 Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction →Structured prediction
- 21:1321:13, 6 December 2018 diff hist +3 m Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction →Introduction
- 21:0821:08, 6 December 2018 diff hist +3 m Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction →Motivation
- 21:0721:07, 6 December 2018 diff hist +28 Mapping Images to Scene Graphs with Permutation-Invariant Structured Prediction →Motivation
- 20:5520:55, 6 December 2018 diff hist −2 m CapsuleNets →Encoder Layers
- 20:2120:21, 6 December 2018 diff hist +1 m CapsuleNets →Notation
- 20:1720:17, 6 December 2018 diff hist +104 CapsuleNets →What is a Capsule
- 19:4419:44, 6 December 2018 diff hist +3 m CapsuleNets →Adversarial Examples
- 19:2119:21, 6 December 2018 diff hist +57 conditional neural process →Model
- 17:5417:54, 6 December 2018 diff hist +8 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Thermometer Coding, [Buckman, 2018] current
- 17:5317:53, 6 December 2018 diff hist 0 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Non-obfuscated Gradients
- 17:5217:52, 6 December 2018 diff hist −1 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →The defense that worked - Adversarial Training [Madry, 2018]
- 17:4917:49, 6 December 2018 diff hist −706 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →The Attacks: Cleanup
- 17:4717:47, 6 December 2018 diff hist +32 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Obfuscated Gradients
- 17:4017:40, 6 December 2018 diff hist −121 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Obfuscated Gradients
- 17:3917:39, 6 December 2018 diff hist 0 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Obfuscated Gradients
- 17:3817:38, 6 December 2018 diff hist 0 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Obfuscated Gradients
- 11:4011:40, 6 December 2018 diff hist +285 DeepVO Towards end to end visual odometry with deep RNN →Critiques
- 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]
- 21:1821:18, 19 November 2018 diff hist −12 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Thermometer Coding, [Buckman, 2018]
- 21:1721:17, 19 November 2018 diff hist +1 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Cascade Adversarial Training, [Na, 2018]
- 21:1621:16, 19 November 2018 diff hist −83 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Recommendations for future defense methods to encourage reproducibility
- 21:0621:06, 19 November 2018 diff hist +2 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →How to check for Obfuscated Gradients
- 21:0421:04, 19 November 2018 diff hist +38 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Obfuscated Gradients
- 20:5320:53, 19 November 2018 diff hist 0 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Adversarial Attacks Terminology
- 20:5120:51, 19 November 2018 diff hist −6 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Introduction
- 20:5020:50, 19 November 2018 diff hist +376 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Introduction
18 November 2018
- 21:1521:15, 18 November 2018 diff hist −20 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Non-obfuscated Gradients
- 18:0818:08, 18 November 2018 diff hist +1 m Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Cascade Adversarial Training [Na, 2018]
- 18:0718:07, 18 November 2018 diff hist +172 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →References
- 18:0518:05, 18 November 2018 diff hist +1 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Cascade Adversarial Training [Na 2018]
- 18:0518:05, 18 November 2018 diff hist −1,253 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Non-obfuscated Gradients
- 17:5217:52, 18 November 2018 diff hist +161 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Other Sources
- 17:5117:51, 18 November 2018 diff hist −157 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Introduction
17 November 2018
- 12:3612:36, 17 November 2018 diff hist +883 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Mitigation Through Randomization, [Xie, 2018]
- 12:2512:25, 17 November 2018 diff hist +1,254 Obfuscated Gradients Give a False Sense of Security Circumventing Defenses to Adversarial Examples →Local Intrinsic Dimensionality, [Ma, 2018]
- 11:0311:03, 17 November 2018 diff hist −145 Countering Adversarial Images Using Input Transformations →Defenses
- 10:5810:58, 17 November 2018 diff hist +3 Countering Adversarial Images Using Input Transformations →Defenses
- 10:5710:57, 17 November 2018 diff hist +23 Countering Adversarial Images Using Input Transformations →Adversarial Attacks
- 10:5510:55, 17 November 2018 diff hist +77 Countering Adversarial Images Using Input Transformations →Adversarial Attacks
- 10:5310:53, 17 November 2018 diff hist +1 Countering Adversarial Images Using Input Transformations →Adversarial Attacks
- 10:5210:52, 17 November 2018 diff hist +163 Countering Adversarial Images Using Input Transformations →Problem Definition
- 10:3510:35, 17 November 2018 diff hist +168 Countering Adversarial Images Using Input Transformations →Terminology
- 10:3510:35, 17 November 2018 diff hist −162 Countering Adversarial Images Using Input Transformations →Problem Definition
- 10:3410:34, 17 November 2018 diff hist 0 Countering Adversarial Images Using Input Transformations →Problem Definition
- 10:3210:32, 17 November 2018 diff hist −3 Countering Adversarial Images Using Input Transformations →Problem Definition/Terminology
- 10:2110:21, 17 November 2018 diff hist +124 Countering Adversarial Images Using Input Transformations →Previous Work
- 10:1410:14, 17 November 2018 diff hist +6 Countering Adversarial Images Using Input Transformations →Motivation
- 10:1310:13, 17 November 2018 diff hist +39 Countering Adversarial Images Using Input Transformations →Introduction