User contributions for Alcateri

12 December 2015

• 23:3123:31, 12 December 2015 diff hist +1,656‎ extracting and Composing Robust Features with Denoising Autoencoders ‎ →‎Analysis of the Denoising Autoencoder: - Included section on Stochastic Operator Perspective
• 22:5022:50, 12 December 2015 diff hist +35‎ m extracting and Composing Robust Features with Denoising Autoencoders ‎ →‎The Denoising Autoencoder: - Cleaned up the wording and mathematical notation in this section
• 20:0520:05, 12 December 2015 diff hist +2,040‎ the loss surfaces of multilayer networks (Choromanska et al.) ‎ Adding a "Prior Work" section, along with showing the references.

2 December 2015

• 16:1316:13, 2 December 2015 diff hist +564‎ on the difficulty of training recurrent neural networks ‎ →‎Background
• 16:0116:01, 2 December 2015 diff hist +40‎ on the difficulty of training recurrent neural networks ‎ →‎Background

23 November 2015

• 13:4013:40, 23 November 2015 diff hist +922‎ learning Fast Approximations of Sparse Coding ‎ →‎Coordinate Descent: Added CoD algorithm and comment about similarity to ISTA

20 November 2015

• 00:2100:21, 20 November 2015 diff hist +2,310‎ show, Attend and Tell: Neural Image Caption Generation with Visual Attention ‎ Adding Related Work section

18 November 2015

• 21:4321:43, 18 November 2015 diff hist +653‎ the Manifold Tangent Classifier ‎ →‎Discussion
• 19:1619:16, 18 November 2015 diff hist +20,731‎ N the Manifold Tangent Classifier ‎ Created page with "== Introduction == The goal in many machine learning problems is to extract information from data with minimal prior knowledge<ref name = "main"> Rifai, S., Dauphin, Y. N., Vinc..."
• 18:1618:16, 18 November 2015 diff hist +44‎ N File:Figure 1 MTC.png ‎ Visualization of the learned tangents of CAE current

16 November 2015

• 13:3913:39, 16 November 2015 diff hist +44‎ f15Stat946PaperSignUp ‎ →‎Set B

12 November 2015

• 12:5412:54, 12 November 2015 diff hist +2,673‎ deep neural networks for acoustic modeling in speech recognition ‎ →‎Generative Pretraining
• 10:2510:25, 12 November 2015 diff hist +622‎ deep neural networks for acoustic modeling in speech recognition ‎ →‎Generative Pretraining: - Introduction of Generative Pretraining

11 November 2015

• 23:2423:24, 11 November 2015 diff hist +31‎ deep neural networks for acoustic modeling in speech recognition ‎No edit summary

30 October 2015

• 11:1511:15, 30 October 2015 diff hist −2‎ human-level control through deep reinforcement learning ‎ →‎The Bellman Equation in the Loss Framework
• 11:1011:10, 30 October 2015 diff hist +3‎ human-level control through deep reinforcement learning ‎ →‎Data & Preprocessing
• 11:0511:05, 30 October 2015 diff hist −34‎ human-level control through deep reinforcement learning ‎ →‎Introduction
• 11:0511:05, 30 October 2015 diff hist −75‎ human-level control through deep reinforcement learning ‎ →‎Introduction
• 11:0411:04, 30 October 2015 diff hist −10‎ human-level control through deep reinforcement learning ‎ →‎Introduction
• 11:0311:03, 30 October 2015 diff hist +769‎ human-level control through deep reinforcement learning ‎No edit summary
• 10:5010:50, 30 October 2015 diff hist −8‎ human-level control through deep reinforcement learning ‎ →‎Results
• 10:5010:50, 30 October 2015 diff hist +238‎ human-level control through deep reinforcement learning ‎ →‎Results with model components removed
• 08:5208:52, 30 October 2015 diff hist +65‎ human-level control through deep reinforcement learning ‎ →‎Results with model components removed
• 08:4908:49, 30 October 2015 diff hist −4‎ human-level control through deep reinforcement learning ‎ →‎Results with model components removed
• 08:4808:48, 30 October 2015 diff hist +737‎ human-level control through deep reinforcement learning ‎No edit summary
• 00:0400:04, 30 October 2015 diff hist +212‎ human-level control through deep reinforcement learning ‎No edit summary
• 00:0000:00, 30 October 2015 diff hist +56‎ N File:Performance.JPG ‎ Performance of DQN vs. Human Tester and Existing Results current

29 October 2015

• 23:5923:59, 29 October 2015 diff hist +873‎ human-level control through deep reinforcement learning ‎No edit summary
• 23:4723:47, 29 October 2015 diff hist +359‎ human-level control through deep reinforcement learning ‎No edit summary
• 23:1123:11, 29 October 2015 diff hist +56‎ N File:QLearning Alg.JPG ‎ The algorithm for deep Q-learning with Experience Replay current
• 23:1023:10, 29 October 2015 diff hist +1,529‎ human-level control through deep reinforcement learning ‎No edit summary
• 22:3622:36, 29 October 2015 diff hist +682‎ human-level control through deep reinforcement learning ‎ →‎Training
• 21:5121:51, 29 October 2015 diff hist +156‎ human-level control through deep reinforcement learning ‎No edit summary
• 21:4921:49, 29 October 2015 diff hist +2‎ human-level control through deep reinforcement learning ‎ →‎Training Details
• 21:4821:48, 29 October 2015 diff hist −3‎ human-level control through deep reinforcement learning ‎ →‎Training Details
• 21:4721:47, 29 October 2015 diff hist −13‎ human-level control through deep reinforcement learning ‎ →‎Training Details
• 21:3121:31, 29 October 2015 diff hist −27‎ human-level control through deep reinforcement learning ‎No edit summary
• 21:2921:29, 29 October 2015 diff hist +1,540‎ human-level control through deep reinforcement learning ‎No edit summary
• 21:0221:02, 29 October 2015 diff hist +66‎ N File:Network Architecture.JPG ‎ A visualization of the deep convolutional network that estimates Q current
• 19:2219:22, 29 October 2015 diff hist +902‎ human-level control through deep reinforcement learning ‎No edit summary
• 18:4218:42, 29 October 2015 diff hist +1‎ human-level control through deep reinforcement learning ‎No edit summary
• 18:4118:41, 29 October 2015 diff hist +1,559‎ human-level control through deep reinforcement learning ‎ →‎Methodology
• 12:3412:34, 29 October 2015 diff hist +5,637‎ N human-level control through deep reinforcement learning ‎ Created page with "== Introduction == Reinforcement learning is "the study of how animals and artificial systems can learn to optimize their behaviour in the face of rewards and punishments" <ref>..."

23 October 2015

• 12:1512:15, 23 October 2015 diff hist +47‎ parsing natural scenes and natural language with recursive neural networks ‎ →‎Recursive Neural Networks for Structure Prediction: Improved readability of math expressions by inserting "\,"

22 October 2015

• 22:2122:21, 22 October 2015 diff hist −2‎ m parsing natural scenes and natural language with recursive neural networks ‎ →‎Input Representation: Adjusted some of the math formatting to make it easier to read.
• 13:5513:55, 22 October 2015 diff hist +1‎ f15Stat946PaperSignUp ‎ →‎Set A
• 13:5513:55, 22 October 2015 diff hist +2‎ f15Stat946PaperSignUp ‎ →‎Set B
• 13:5413:54, 22 October 2015 diff hist 0‎ f15Stat946PaperSignUp ‎ →‎Set B
• 13:5313:53, 22 October 2015 diff hist +122‎ f15Stat946PaperSignUp ‎No edit summary

13 October 2015

• 23:2123:21, 13 October 2015 diff hist 0‎ proposal for STAT946 (Deep Learning) final projects Fall 2015 ‎No edit summary