# Difference between revisions of "f17Stat946PaperSignUp"

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E: You had an editorial 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). | ||

− | [https:// | + | [https://docs.google.com/forms/d/e/1FAIpQLSf9DIuUylcR-HCN_ts-uP-10jE4wDuMuzTA4vg3r2KR_uHRWQ/viewform?vc=0&c=0&w=1J Your feedback on presentations] |

=Paper presentation= | =Paper presentation= | ||

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|Oct 12 (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] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary] | |Oct 12 (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] || [http://wikicoursenote.com/wiki/Stat946f15/Sequence_to_sequence_learning_with_neural_networks#Long_Short-Term_Memory_Recurrent_Neural_Network Summary] | ||

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− | |Oct | + | |Oct 26 || Sakif Khan || 1|| Improved Variational Inference with Inverse Autoregressive Flow || [https://papers.nips.cc/paper/6581-improved-variational-inference-with-inverse-autoregressive-flow Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Improved_Variational_Inference_with_Inverse_Autoregressive_Flow Summary] |

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− | |Oct | + | |Oct 26 || Amir-Hossein Karimi ||2 || Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling || [https://papers.nips.cc/paper/6096-learning-a-probabilistic-latent-space-of-object-shapes-via-3d-generative-adversarial-modeling Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Learning_a_Probabilistic_Latent_Space_of_Object_Shapes_via_3D_GAN Summary] |

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− | |Oct 26 || || | + | |Oct 26 ||Josh Valchar || 3|| Learning What and Where to Draw ||[https://papers.nips.cc/paper/6111-learning-what-and-where-to-draw] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Learning_What_and_Where_to_Draw Summary] |

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− | |Oct | + | |Oct 31 ||Jimit Majmudar ||4 ||Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition || [https://papers.nips.cc/paper/6258-incremental-boosting-convolutional-neural-network-for-facial-action-unit-recognition.pdf Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Incremental_Boosting_Convolutional_Neural_Network_for_Facial_Action_Unit_Recognition Summary] |

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− | |Oct | + | |Oct 31 || ||6 || || || |

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− | | | + | |Nov 2 || Prashanth T.K. || 7|| When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications||[http://proceedings.mlr.press/v70/zhou17c.html Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=When_can_Multi-Site_Datasets_be_Pooled_for_Regression%3F_Hypothesis_Tests,_l2-consistency_and_Neuroscience_Applications:_Summary Summary] |

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− | | | + | |Nov 2 || |||| || || |

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− | | | + | |Nov 2 || Haotian Lyu || 9||Learning Important Features Through Propagating Activation Differences|| [http://proceedings.mlr.press/v70/shrikumar17a/shrikumar17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Learning_Important_Features_Through_Propagating_Activation_Differences summary] |

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− | |Nov | + | |Nov 7 || Dishant Mittal ||10 ||meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting|| [https://arxiv.org/pdf/1706.06197.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=meProp:_Sparsified_Back_Propagation_for_Accelerated_Deep_Learning_with_Reduced_Overfitting Summary] |

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− | |Nov | + | |Nov 7 || Omid Rezai|| 11 || Understanding the Effective Receptive Field in Deep Convolutional Neural Networks || [https://papers.nips.cc/paper/6203-understanding-the-effective-receptive-field-in-deep-convolutional-neural-networks.pdf Paper]|| [[Understanding the Effective Receptive Field in Deep Convolutional Neural Networks | Summary]] |

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− | |Nov | + | |Nov 7 || Rahul Iyer|| 12|| Convolutional Sequence to Sequence Learning || [https://arxiv.org/pdf/1705.03122.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Convolutional_Sequence_to_Sequence_Learning Summary] |

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− | |Nov | + | |Nov 9 || ShuoShuo Liu ||13 ||Learning the Number of Neurons in Deep Networks|| [http://papers.nips.cc/paper/6372-learning-the-number-of-neurons-in-deep-networks.pdf Paper] || [[Learning the Number of Neurons in Deep Networks | Summary]] |

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− | |Nov | + | |Nov 9 || Aravind Balakrishnan ||14 || FeUdal Networks for Hierarchical Reinforcement Learning || [http://proceedings.mlr.press/v70/vezhnevets17a/vezhnevets17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=FeUdal_Networks_for_Hierarchical_Reinforcement_Learning Summary] |

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− | |Nov | + | |Nov 9 || Varshanth R Rao ||15 || Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study || [http://proceedings.mlr.press/v70/ritter17a/ritter17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Cognitive_Psychology_For_Deep_Neural_Networks:_A_Shape_Bias_Case_Study Summary] |

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− | |Nov | + | |Nov 14 || Avinash Prasad ||16 || Coupled GAN|| [https://papers.nips.cc/paper/6544-coupled-generative-adversarial-networks.pdf] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Coupled_GAN Summary] |

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− | |Nov | + | |Nov 14 || Nafseer Kadiyaravida ||17 || Dialog-based Language Learning || [https://papers.nips.cc/paper/6264-dialog-based-language-learning.pdf Paper] || [[Dialog-based Language Learning | Summary]] |

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− | |Nov | + | |Nov 14 || Ruifan Yu ||18 || Imagination-Augmented Agents for Deep Reinforcement Learning || [https://arxiv.org/pdf/1707.06203.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Imagination-Augmented_Agents_for_Deep_Reinforcement_Learning Summary] |

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− | |Nov | + | |Nov 16 || Hamidreza Shahidi ||19 || Teaching Machines to Describe Images via Natural Language Feedback || [https://arxiv.org/pdf/1706.00130 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Teaching_Machines_to_Describe_Images_via_Natural_Language_Feedback Summary] |

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− | |Nov | + | |Nov 16 || Sachin vernekar ||20 || "Why Should I Trust You?": Explaining the Predictions of Any Classifier || [https://arxiv.org/abs/1602.04938 Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=%22Why_Should_I_Trust_You%3F%22:_Explaining_the_Predictions_of_Any_Classifier Summary] |

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− | |Nov | + | |Nov 16 || Yunqing He ||21 || LightRNN: Memory and Computation-Efficient Recurrent Neural Networks || [https://papers.nips.cc/paper/6512-lightrnn-memory-and-computation-efficient-recurrent-neural-networks] |

+ | || [[LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | Summary]] | ||

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− | |Nov | + | ||Nov 21 ||Aman Jhunjhunwala ||22 ||Modular Multitask Reinforcement Learning with Policy Sketches ||[http://proceedings.mlr.press/v70/andreas17a/andreas17a.pdf Paper]||[[Modular Multitask Reinforcement Learning with Policy Sketches | Summary]] |

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− | |Nov | + | |Nov 21 || Michael Honke ||23 || Universal Style Transfer via Feature Transforms|| [https://arxiv.org/pdf/1705.08086.pdf Paper] ||| [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Universal_Style_Transfer_via_Feature_Transforms Summary] |

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− | |Nov | + | |Nov 21 || Venkateshwaran Balasubramanian ||24 || Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition || [https://papers.nips.cc/paper/6335-deep-alternative-neural-network-exploring-contexts-as-early-as-possible-for-action-recognition.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Deep_Alternative_Neural_Network:_Exploring_Contexts_As_Early_As_Possible_For_Action_Recognition Summary] |

− | |||

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− | + | |Nov 23 || Ashish Gaurav ||25 || Deep Exploration via Bootstrapped DQN || [https://papers.nips.cc/paper/6501-deep-exploration-via-bootstrapped-dqn.pdf Paper] ||[[Deep Exploration via Bootstrapped DQN | Summary]] | |

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− | |Nov | + | |Nov 23 || Ershad Banijamali||26 ||Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ||[http://proceedings.mlr.press/v70/finn17a/finn17a.pdf Paper] || [[Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Summary]] |

|- | |- | ||

− | |Nov | + | |Nov 23 || Dylan Spicker || 27|| Unsupervised Domain Adaptation with Residual Transfer Networks || [https://papers.nips.cc/paper/6110-unsupervised-domain-adaptation-with-residual-transfer-networks.pdf Paper] || [[Unsupervised Domain Adaptation with Residual Transfer Networks | Summary]] |

|- | |- | ||

− | |Nov | + | |Nov 28 || Mike Rudd || 28 || Conditional Image Synthesis with Auxiliary Classifier GANs || [http://proceedings.mlr.press/v70/odena17a.html Paper] || [[Conditional Image Synthesis with Auxiliary Classifier GANs | Summary]] |

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− | |Nov | + | |Nov 28 || Shivam Kalra ||29 || Hierarchical Question-Image Co-Attention for Visual Question Answering || [https://arxiv.org/pdf/1606.00061.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=Hierarchical_Question-Image_Co-Attention_for_Visual_Question_Answering Summary] |

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− | |Nov | + | |Nov 28 || Aditya Sriram ||30 ||Conditional Image Generation with PixelCNN Decoders|| [https://papers.nips.cc/paper/6527-conditional-image-generation-with-pixelcnn-decoders.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Conditional_Image_Generation_with_PixelCNN_Decoders Summary] |

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− | |Nov | + | |Nov 30 || Congcong Zhi ||31 || Decoding with Value Networks for Neural Machine Translation |

+ | || [https://nips.cc/Conferences/2017/Schedule?showEvent=8815 Paper] ||[https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/Decoding_with_Value_Networks_for_Neural_Machine_Translation Summary] | ||

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− | |Nov | + | |Nov 30 || Jian Deng || 32|| Automated Curriculum Learning for Neural Networks || [http://proceedings.mlr.press/v70/graves17a/graves17a.pdf Paper] || [https://wiki.math.uwaterloo.ca/statwiki/index.php?title=STAT946F17/_Automated_Curriculum_Learning_for_Neural_Networks Summary] |

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− | + | |Nov 30 ||Elaheh Jalalpour || 33|| || || | |

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## Latest revision as of 19:28, 19 November 2020

# List of Papers

# Record your contributions here:

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 |

Oct 12 (example) | Ri Wang | Sequence to sequence learning with neural networks. | Paper | Summary | |

Oct 26 | Sakif Khan | 1 | Improved Variational Inference with Inverse Autoregressive Flow | Paper | Summary |

Oct 26 | Amir-Hossein Karimi | 2 | Learning a Probabilistic Latent Space of Object Shapes via 3D Generative-Adversarial Modeling | Paper | Summary |

Oct 26 | Josh Valchar | 3 | Learning What and Where to Draw | [1] | Summary |

Oct 31 | Jimit Majmudar | 4 | Incremental Boosting Convolutional Neural Network for Facial Action Unit Recognition | Paper | Summary |

Oct 31 | 6 | ||||

Nov 2 | Prashanth T.K. | 7 | When can Multi-Site Datasets be Pooled for Regression? Hypothesis Tests, l2-consistency and Neuroscience Applications | Paper | Summary |

Nov 2 | |||||

Nov 2 | Haotian Lyu | 9 | Learning Important Features Through Propagating Activation Differences | Paper | summary |

Nov 7 | Dishant Mittal | 10 | meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting | Paper | Summary |

Nov 7 | Omid Rezai | 11 | Understanding the Effective Receptive Field in Deep Convolutional Neural Networks | Paper | Summary |

Nov 7 | Rahul Iyer | 12 | Convolutional Sequence to Sequence Learning | Paper | Summary |

Nov 9 | ShuoShuo Liu | 13 | Learning the Number of Neurons in Deep Networks | Paper | Summary |

Nov 9 | Aravind Balakrishnan | 14 | FeUdal Networks for Hierarchical Reinforcement Learning | Paper | Summary |

Nov 9 | Varshanth R Rao | 15 | Cognitive Psychology for Deep Neural Networks: A Shape Bias Case Study | Paper | Summary |

Nov 14 | Avinash Prasad | 16 | Coupled GAN | [2] | Summary |

Nov 14 | Nafseer Kadiyaravida | 17 | Dialog-based Language Learning | Paper | Summary |

Nov 14 | Ruifan Yu | 18 | Imagination-Augmented Agents for Deep Reinforcement Learning | Paper | Summary |

Nov 16 | Hamidreza Shahidi | 19 | Teaching Machines to Describe Images via Natural Language Feedback | Paper | Summary |

Nov 16 | Sachin vernekar | 20 | "Why Should I Trust You?": Explaining the Predictions of Any Classifier | Paper | Summary |

Nov 16 | Yunqing He | 21 | LightRNN: Memory and Computation-Efficient Recurrent Neural Networks | [3] | Summary |

Nov 21 | Aman Jhunjhunwala | 22 | Modular Multitask Reinforcement Learning with Policy Sketches | Paper | Summary |

Nov 21 | Michael Honke | 23 | Universal Style Transfer via Feature Transforms | Paper | Summary |

Nov 21 | Venkateshwaran Balasubramanian | 24 | Deep Alternative Neural Network: Exploring Contexts as Early as Possible for Action Recognition | Paper | Summary |

Nov 23 | Ashish Gaurav | 25 | Deep Exploration via Bootstrapped DQN | Paper | Summary |

Nov 23 | Ershad Banijamali | 26 | Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks | Paper | Summary |

Nov 23 | Dylan Spicker | 27 | Unsupervised Domain Adaptation with Residual Transfer Networks | Paper | Summary |

Nov 28 | Mike Rudd | 28 | Conditional Image Synthesis with Auxiliary Classifier GANs | Paper | Summary |

Nov 28 | Shivam Kalra | 29 | Hierarchical Question-Image Co-Attention for Visual Question Answering | Paper | Summary |

Nov 28 | Aditya Sriram | 30 | Conditional Image Generation with PixelCNN Decoders | Paper | Summary |

Nov 30 | Congcong Zhi | 31 | Decoding with Value Networks for Neural Machine Translation | Paper | Summary |

Nov 30 | Jian Deng | 32 | Automated Curriculum Learning for Neural Networks | Paper | Summary |

Nov 30 | Elaheh Jalalpour | 33 |