# Difference between revisions of "stat940F21"

From statwiki

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|Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539] | |Sep 15 (example)||Ri Wang || ||Sequence to sequence learning with neural networks.||[http://papers.nips.cc/paper/5346-sequence-to-sequence-learning-with-neural-networks.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Going_Deeper_with_Convolutions Summary] || [https://youtu.be/JWozRg_X-Vg?list=PLehuLRPyt1HzXDemu7K4ETcF0Ld_B5adG&t=539] | ||

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− | |Week of Nov 11 || | + | |Week of Nov 11 || || 1|| || || || |

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− | |Week of Nov | + | |Week of Nov 11 || Abhinav Chanana || 2||AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity || [https://openreview.net/pdf?id=S1gmrxHFvB Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Augmix:_New_Data_Augmentation_method_to_increase_the_robustness_of_the_algorithm#Conclusion Summary] || [[https://youtu.be/epBzlXHFNlY Presentation ]] |

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− | |Week of Nov | + | |Week of Nov 11 || Maziar Dadbin || 3|| ALBERT: A Lite BERT for Self-supervised Learning of Language Representations || [https://openreview.net/pdf?id=H1eA7AEtvS paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=ALBERT:_A_Lite_BERT_for_Self-supervised_Learning_of_Language_Representations Summary]|| |

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− | |Week of Nov | + | |Week of Nov 11 ||John Landon Edwards || 4||From Variational to Deterministic Autoencoders ||[http://www.openreview.net/pdf?id=S1g7tpEYDS Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=From_Variational_to_Deterministic_Autoencoders#Redesigned_Training_Loss_Function Summary] || [https://youtu.be/yW4eu3FWqIc Presentation] |

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− | |Week of Nov | + | |Week of Nov 11 ||Wenyu Shen || 5|| Pre-training of Deep Bidirectional Transformers for Language Understanding || [https://arxiv.org/pdf/1810.04805.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F20/BERT:_Pre-training_of_Deep_Bidirectional_Transformers_for_Language_Understanding Summary] || [https://www.youtube.com/watch?v=vF5EoIFd2D8 Presentation video] |

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− | |Week of Nov | + | |Week of Nov 11 || Syed Saad Naseem || 6|| Learning The Difference That Makes A Difference With Counterfactually-Augmented Data|| [https://openreview.net/pdf?id=Sklgs0NFvr Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_The_Difference_That_Makes_A_Difference_With_Counterfactually-Augmented_Data Summary] || [https://youtu.be/bKC2BiTuSTQ Presentation video] |

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|Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] || | |Week of Nov 9 || Donya Hamzeian || 7|| The Curious Case of Neural Text Degeneration || [https://iclr.cc/virtual_2020/poster_rygGQyrFvH.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=The_Curious_Case_of_Degeneration Summary] || |

## Revision as of 19:16, 5 September 2021

## Project Proposal

# Paper presentation

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

Sep 15 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | [1] | |

Week of Nov 11 | 1 | |||||

Week of Nov 11 | Abhinav Chanana | 2 | AUGMIX: A Simple Data Procession method to Improve Robustness And Uncertainity | Paper | Summary | [Presentation ] |

Week of Nov 11 | Maziar Dadbin | 3 | ALBERT: A Lite BERT for Self-supervised Learning of Language Representations | paper | Summary | |

Week of Nov 11 | John Landon Edwards | 4 | From Variational to Deterministic Autoencoders | Paper | Summary | Presentation |

Week of Nov 11 | Wenyu Shen | 5 | Pre-training of Deep Bidirectional Transformers for Language Understanding | Paper | Summary | Presentation video |

Week of Nov 11 | Syed Saad Naseem | 6 | Learning The Difference That Makes A Difference With Counterfactually-Augmented Data | Paper | Summary | Presentation video |

Week of Nov 9 | Donya Hamzeian | 7 | The Curious Case of Neural Text Degeneration | Paper | Summary | |

Week of Nov 9 | Parsa Torabian | 8 | Orthogonal Gradient Descent for Continual Learning | Paper | Summary | Learn |

Week of Nov 9 | Arash Moayyedi | 9 | When Does Self-supervision Improve Few-shot Learning? | Paper | Summary | Learn |

Week of Nov 9 | Parsa Ashrafi Fashi | 10 | Learning to Generalize: Meta-Learning for Domain Generalization | Paper | Summary | Presentation Video |

Week of Nov 9 | Jaskirat Singh Bhatia | 11 | A FAIRCOMPARISON OFGRAPHNEURALNETWORKSFORGRAPHCLASSIFICATION | Paper | Summary | Presentation |

Week of Nov 9 | Gaurav Sikri | 12 | BREAKING CERTIFIED DEFENSES: SEMANTIC ADVERSARIAL EXAMPLES WITH SPOOFED ROBUSTNESS CERTIFICATES | Paper | Summary | [Presentation ] |

Week of Nov 16 | Abhinav Jain | 13 | The Logical Expressiveness of Graph Neural Networks | Paper | Summary | Presentation |

Week of Nov 16 | Gautam Bathla | 14 | One-Shot Object Detection with Co-Attention and Co-Excitation | Paper | Summary | Presentation |

Week of Nov 16 | Shikhar Sakhuja | 15 | SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems | Paper | Summary | [Presentation ] |

Week of Nov 16 | Cameron Meaney | 16 | Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations | Paper | Summary | Learn |

Week of Nov 16 | Sobhan Hemati | 17 | Adversarial Fisher Vectors for Unsupervised Representation Learning | Paper | Summary | video |

Week of Nov 16 | Milad Sikaroudi | 18 | Domain Genralization via Model Agnostic Learning of Semantic Features | Paper | Summary | video also available on Learn |

Week of Nov 23 | Bowen You | 19 | DREAM TO CONTROL: LEARNING BEHAVIORS BY LATENT IMAGINATION | Paper | Summary | Learn |

Week of Nov 23 | Nouha Chatti | 20 | This Looks Like That: Deep Learning for Interpretable Image Recognition | Paper | Summary | |

Week of Nov 23 | Mohan Wu | 21 | Pretrained Generalized Autoregressive Model with Adaptive Probabilistic Label Cluster for Extreme Multi-label Text Classification | Paper | Summary | video |

Week of Nov 23 | Xinyi Yan | 22 | Dense Passage Retrieval for Open-Domain Question Answering | Paper | Summary | Learn |

Week of Nov 23 | Meixi Chen | 23 | Functional Regularisation for Continual Learning with Gaussian Processes | Paper | Summary | Learn |

Week of Nov 23 | Ahmed Salamah | 24 | AdaCompress: Adaptive Compression for Online Computer Vision Services | Paper | Summary | video or Learn |

Week of Nov 23 | Mohammad Mahmoud | 32 | Mathematical Reasoning in Latent Space | [2] | ||

Week of Nov 30 | Danial Maleki | 25 | RoBERTa: A Robustly Optimized BERT Pretraining Approach | Paper | Summary | Presentation Video |

Week of Nov 30 | Gursimran Singh | 26 | BERTScore: Evaluating Text Generation with BERT | Paper | Summary | Learn |

Week of Nov 30 | Govind Sharma | 27 | Time-series Generative Adversarial Networks | Paper | Summary | video or Learn |

Week of Nov 30 | Maral Rasoolijaberi | 28 | A critical analysis of self-supervision, or what we can learn from a single image | Paper | Summary | YouTube |

Week of Nov 30 | Sina Farsangi | 29 | Self-Supervised Learning of Pretext-Invariant Representations | Paper | Summary | YouTube or Learn |

Week of Nov 30 | Pierre McWhannel | 30 | Pre-training Tasks for Embedding-based Large-scale Retrieval | Paper | Summary | Learn |

Week of Nov 30 | Wenjuan Qi | 31 | Network Deconvolution | Paper | placeholder |