User contributions for Isucholu
Jump to navigation
Jump to search
2 April 2018
- 21:2021:20, 2 April 2018 diff hist +190 Understanding Image Motion with Group Representations →Criticism
- 21:1721:17, 2 April 2018 diff hist +15 Understanding Image Motion with Group Representations →Introduction
- 21:1521:15, 2 April 2018 diff hist −6 On The Convergence Of ADAM And Beyond →Introduction
- 21:0921:09, 2 April 2018 diff hist +202 stat946w18/Self Normalizing Neural Networks →Introduction and Motivation
- 21:0421:04, 2 April 2018 diff hist +240 Word translation without parallel data →Conclusion
- 20:5820:58, 2 April 2018 diff hist +61 stat946w18/Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolutional Layers →Introduction
- 20:5320:53, 2 April 2018 diff hist +149 stat946w18/AmbientGAN: Generative Models from Lossy Measurements →Future Research
- 20:5120:51, 2 April 2018 diff hist +3 stat946w18/AmbientGAN: Generative Models from Lossy Measurements →Model
- 20:4620:46, 2 April 2018 diff hist +13 stat946w18/Spectral normalization for generative adversial network →Conclusions
- 20:4020:40, 2 April 2018 diff hist +7 Training And Inference with Integers in Deep Neural Networks →Conclusion
- 20:4020:40, 2 April 2018 diff hist +164 Training And Inference with Integers in Deep Neural Networks →Conclusion
- 20:3620:36, 2 April 2018 diff hist +174 Dynamic Routing Between Capsules STAT946 →Weakness of Capsule Network
- 20:3120:31, 2 April 2018 diff hist +26 stat946w18/Tensorized LSTMs →A Quick Introduction to RNN and LSTM
- 20:2820:28, 2 April 2018 diff hist −114 stat946w18/Tensorized LSTMs →A Quick Introduction to RNN and LSTM
23 March 2018
- 21:4821:48, 23 March 2018 diff hist +39 Do Deep Neural Networks Suffer from Crowding →What is the problem in CNNs?
- 21:4621:46, 23 March 2018 diff hist +72 Do Deep Neural Networks Suffer from Crowding →Critique
21 March 2018
- 00:2400:24, 21 March 2018 diff hist +118 stat946w18/Implicit Causal Models for Genome-wide Association Studies →Conclusion
- 00:2300:23, 21 March 2018 diff hist −17 stat946w18/Implicit Causal Models for Genome-wide Association Studies →Critique
- 00:0700:07, 21 March 2018 diff hist +54 stat946w18/Implicit Causal Models for Genome-wide Association Studies →Introduction and Motivation
- 00:0100:01, 21 March 2018 diff hist 0 Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments →Setup
- 00:0000:00, 21 March 2018 diff hist +76 Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments →Training Spiders to Fight Each Other (Adversarial Meta-Learning)
20 March 2018
- 18:4318:43, 20 March 2018 diff hist +16 Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments →Introduction
15 March 2018
- 12:5512:55, 15 March 2018 diff hist +9 stat946w18/Synthetic and natural noise both break neural machine translation →Conclusion
- 12:5412:54, 15 March 2018 diff hist +7 stat946w18/Synthetic and natural noise both break neural machine translation →Conclusion
- 12:5412:54, 15 March 2018 diff hist +65 stat946w18/Synthetic and natural noise both break neural machine translation →Structure Invariant Representations
- 12:5112:51, 15 March 2018 diff hist 0 stat946w18/Synthetic and natural noise both break neural machine translation →Natural Noise
- 12:5012:50, 15 March 2018 diff hist −8 stat946w18/Synthetic and natural noise both break neural machine translation →Natural Noise
- 12:1012:10, 15 March 2018 diff hist −19 A Neural Representation of Sketch Drawings →Applications and Future Work
- 12:0412:04, 15 March 2018 diff hist +279 stat946w18/Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data →Criticism
1 March 2018
- 03:0403:04, 1 March 2018 diff hist −2 m One-Shot Imitation Learning →Experiments
- 01:1601:16, 1 March 2018 diff hist +1 m One-Shot Imitation Learning →Neighborhood Attention:
23 February 2018
- 01:5701:57, 23 February 2018 diff hist +86 stat946w18/Unsupervised Machine Translation Using Monolingual Corpora Only Undo revision 32126 by Isucholu (talk)
- 00:0400:04, 23 February 2018 diff hist −86 stat946w18/Unsupervised Machine Translation Using Monolingual Corpora Only →Word vector alignment
- 00:0200:02, 23 February 2018 diff hist −50 stat946w18/Unsupervised Machine Translation Using Monolingual Corpora Only →Overview of unsupervised translation system
22 February 2018
- 23:5823:58, 22 February 2018 diff hist −2 One-Shot Imitation Learning →Attention over current state:
- 23:5623:56, 22 February 2018 diff hist +38 One-Shot Imitation Learning →Manipulation network
- 23:5523:55, 22 February 2018 diff hist +12 One-Shot Imitation Learning →Manipulation network
- 23:4823:48, 22 February 2018 diff hist +116 stat946w18 →Paper presentation
- 23:4723:47, 22 February 2018 diff hist 0 One-Shot Imitation Learning →Performance Evaluation
- 23:4623:46, 22 February 2018 diff hist +7 One-Shot Imitation Learning →Performance Evaluation
- 23:4623:46, 22 February 2018 diff hist +1 One-Shot Imitation Learning →Architecture
- 23:4623:46, 22 February 2018 diff hist +1 One-Shot Imitation Learning →One-Shot Imitation Learning
- 23:4623:46, 22 February 2018 diff hist +2 One-Shot Imitation Learning →One-Shot Imitation Learning
- 23:4523:45, 22 February 2018 diff hist +1,916 One-Shot Imitation Learning →Neighborhood Attention:
- 23:0023:00, 22 February 2018 diff hist +239 One-Shot Imitation Learning →References
- 22:5622:56, 22 February 2018 diff hist +1,394 One-Shot Imitation Learning →Criticisms
- 22:4222:42, 22 February 2018 diff hist +1,477 One-Shot Imitation Learning →Context network
- 22:0522:05, 22 February 2018 diff hist +320 One-Shot Imitation Learning →Performance Evaluation
- 22:0022:00, 22 February 2018 diff hist +486 N File:oneshot3.jpg From [https://papers.nips.cc/paper/6709-one-shot-imitation-learning.pdf (Duan et al. 2017)] Figure 3: Comparison of different conditioning strategies. The darkest bar shows the performance of the hard-coded policy, which unsurprisingly performs the bes... current
- 21:5921:59, 22 February 2018 diff hist +1,565 One-Shot Imitation Learning →Experiments
