# Difference between revisions of "stat946w18"

From statwiki

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|Mar 29 || Alex Pon || 28||PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space || [https://arxiv.org/abs/1706.02413 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=PointNet%2B%2B:_Deep_Hierarchical_Feature_Learning_on_Point_Sets_in_a_Metric_Space Summary] | |Mar 29 || Alex Pon || 28||PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space || [https://arxiv.org/abs/1706.02413 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=PointNet%2B%2B:_Deep_Hierarchical_Feature_Learning_on_Point_Sets_in_a_Metric_Space Summary] | ||

|- | |- | ||

− | |Mar 29 || Sean Walsh || 29||Multi-scale Dense Networks for Resource Efficient Image Classification || [https://arxiv.org/pdf/1703.09844.pdf Paper] || | + | |Mar 29 || Sean Walsh || 29||Multi-scale Dense Networks for Resource Efficient Image Classification || [https://arxiv.org/pdf/1703.09844.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Multi-scale_Dense_Networks_for_Resource_Efficient_Image_Classification#Architecture Summary] |

|- | |- | ||

|Mar 29 || Jason Ku || 30||MarrNet: 3D Shape Reconstruction via 2.5D Sketches || [https://arxiv.org/pdf/1711.03129.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=MarrNet:_3D_Shape_Reconstruction_via_2.5D_Sketches Summary] | |Mar 29 || Jason Ku || 30||MarrNet: 3D Shape Reconstruction via 2.5D Sketches || [https://arxiv.org/pdf/1711.03129.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=MarrNet:_3D_Shape_Reconstruction_via_2.5D_Sketches Summary] |

## Revision as of 20:39, 22 March 2018

# List of Papers

# Record your contributions here [1]

Use the following notations:

P: You have written a summary/critique on the paper.

T: You had a technical contribution on a paper (excluding the paper that you present).

E: You had an editorial contribution on a paper (excluding the paper that you present).

Your feedback on presentations

# Paper presentation

Date | Name | Paper number | Title | Link to the paper | Link to the summary |

Feb 15 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | |

Feb 27 | 1 | ||||

Feb 27 | 2 | ||||

Feb 27 | 3 | ||||

Mar 1 | Peter Forsyth | 4 | Unsupervised Machine Translation Using Monolingual Corpora Only | Paper | [Summary] |

Mar 1 | wenqing liu | 5 | Spectral Normalization for Generative Adversarial Networks | Paper | Summary |

Mar 1 | Ilia Sucholutsky | 6 | One-Shot Imitation Learning | Paper | Summary |

Mar 6 | George (Shiyang) Wen | 7 | AmbientGAN: Generative models from lossy measurements | Paper | Summary |

Mar 6 | Raphael Tang | 8 | Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolutional Layers | Paper | Summary |

Mar 6 | Fan Xia | 9 | Word translation without parallel data | Paper | Summary |

Mar 8 | Alex (Xian) Wang | 10 | Self-Normalizing Neural Networks | Paper | Summary |

Mar 8 | Michael Broughton | 11 | Convergence of Adam and beyond | Paper | Summary |

Mar 8 | Wei Tao Chen | 12 | Predicting Floor-Level for 911 Calls with Neural Networks and Smartphone Sensor Data | Paper | Summary |

Mar 13 | Chunshang Li | 13 | UNDERSTANDING IMAGE MOTION WITH GROUP REPRESENTATIONS | Paper | Summary |

Mar 13 | Saifuddin Hitawala | 14 | Robust Imitation of Diverse Behaviors | Paper | Summary |

Mar 13 | Taylor Denouden | 15 | A neural representation of sketch drawings | Paper | Summary |

Mar 15 | Zehao Xu | 16 | Synthetic and natural noise both break neural machine translation | Paper | Summary |

Mar 15 | Prarthana Bhattacharyya | 17 | Wasserstein Auto-Encoders | Paper | Summary |

Mar 15 | Changjian Li | 18 | Label-Free Supervision of Neural Networks with Physics and Domain Knowledge | Paper | Summary |

Mar 20 | Travis Dunn | 19 | Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments | Paper | Summary |

Mar 20 | Sushrut Bhalla | 20 | MaskRNN: Instance Level Video Object Segmentation | Paper | Summary |

Mar 20 | Hamid Tahir | 21 | Wavelet Pooling for Convolution Neural Networks | Paper | Summary |

Mar 22 | Dongyang Yang | 22 | Implicit Causal Models for Genome-wide Association Studies | Paper | Summary |

Mar 22 | Yao Li | 23 | Improving GANs Using Optimal Transport | Paper | Summary |

Mar 22 | Sahil Pereira | 24 | End-to-End Differentiable Adversarial Imitation Learning | Paper | Summary |

Mar 27 | Jaspreet Singh Sambee | 25 | Do Deep Neural Networks Suffer from Crowding? | Paper | Summary |

Mar 27 | Braden Hurl | 26 | Spherical CNNs | Paper | Summary |

Mar 27 | Marko Ilievski | 27 | Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders | Paper | Summary |

Mar 29 | Alex Pon | 28 | PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space | Paper | Summary |

Mar 29 | Sean Walsh | 29 | Multi-scale Dense Networks for Resource Efficient Image Classification | Paper | Summary |

Mar 29 | Jason Ku | 30 | MarrNet: 3D Shape Reconstruction via 2.5D Sketches | Paper | Summary |

Apr 3 | Tong Yang | 31 | Dynamic Routing Between Capsules. | Paper | |

Apr 3 | Benjamin Skikos | 32 | Training and Inference with Integers in Deep Neural Networks | Paper | |

Apr 3 | Weishi Chen | 33 | Tensorized LSTMs for Sequence Learning | Paper | Summary |