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