Lunch and Poster Session

1- Uniform convergence may be unable to explain generalization in deep learning. Vaishnavh Nagarajan and J. Zico Kolter 2- The effects of optimization on generalization in infinitely wide neural networks. Anastasia Borovykh 3- Generalized Capsule Networks with Trainable Routing Procedure. Zhenhua Chen, Chuhua Wang, David Crandall and Tiancong Zhao 4- Implicit Regularization of Discrete Gradient Dynamics in Deep Linear Neural Networks. Gauthier Gidel, Francis Bach and Simon Lacoste-Julien 5- Stable Rank Normalization for Improved Generalization in Neural Networks. Amartya Sanyal, Philip H Torr and Puneet K Dokania 6- On improving deep learning generalization with adaptive sparse connectivity. Shiwei Liu, Decebal Constantin Mocanu and Mykola Pechenizkiy 7- Identity Connections in Residual Nets Improve Noise Stability. Shuzhi Yu and Carlo Tomasi 8- Factors for the Generalisation of Identity Relations by Neural Networks. Radha Manisha Kopparti and Tillman Weyde 9- Output-Constrained Bayesian Neural Networks. Wanqian Yang, Lars Lorch, Moritz A. Graule, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier and Finale Doshi-Velez 10- An Empirical Study on Hyperparameters and their Interdependence for RL Generalization. Xingyou Song, Yilun Du and Jacob Jackson 11- Towards Large Scale Structure of the Loss Landscape of Neural Networks. Stanislav Fort and Stanislaw Jastrzebski 12- Detecting Extrapolation with Influence Functions. David Madras, James Atwood and Alex D'Amour 13- Towards Task and Architecture-Independent Generalization Gap Predictors. Scott Yak, Hanna Mazzawi and Javier Gonzalvo 14- SGD Picks a Stable Enough Trajectory. Stanisław Jastrzębski and Stanislav Fort 15- MazeNavigator: A Customisable 3D Benchmark for Assessing Generalisation in Reinforcement Learning. Luke Harries, Sebastian Lee, Jaroslaw Rzepecki, Katja Hofmann and Sam Devlin 16- Utilizing Eye Gaze to Enhance the Generalization of Imitation Network to Unseen Environments. Congcong Liu, Yuying Chen, Lei Tai, Ming Liu and Bertram Shi 17- Investigating Under and Overfitting in Wasserstein Generative Adversarial Networks. Ben Adlam, Charles Weill and Amol Kapoor 18- An Empirical Evaluation of Adversarial Robustness under Transfer Learning. Todor Davchev, Timos Korres, Stathi Fotiadis, Nick Antonopoulos and Subramanian Ramamoorthy 19- On Adversarial Robustness of Small vs Large Batch Training. Sandesh Kamath, Amit Deshpande and K V Subrahmanyam 20- The Principle of Unchanged Optimality in Reinforcement Learning Generalization. Xingyou Song and Alex Irpan 21- On the Generalization Capability of Memory Networks for Reasoning. Monireh Ebrahimi, Md Kamruzzaman Sarker, Federico Bianchi, Ning Xie, Aaron Eberhart, Derek Doran and Pascal Hitzler 22- Visualizing How Embeddings Generalize. Xiaotong Liu, Hong Xuan, Zeyu Zhang, Abby Stylianou and Robert Pless 23- Theoretical Analysis of the Fixup Initialization for Fast Convergence and High Generalization Ability. Yasutaka Furusho and Kazushi Ikeda 24- Data-Dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation. Colin Wei and Tengyu Ma 25- Few-Shot Transfer Learning From Multiple Pre-Trained Networks. Joshua Ka-Wing Lee, Prasanna Sattigeri and Gregory Wornell 26- Uniform Stability and High Order Approximation of SGLD in Non-Convex Learning. Mufan Li and Maxime Gazeau 27- Better Generalization with Adaptive Adversarial Training. Amit Despande, Sandesh Kamath and K V Subrahmanyam 28- Adversarial Training Generalizes Spectral Norm Regularization. Kevin Roth, Yannic Kilcher and Thomas Hofmann 29- A Causal View on Robustness of Neural Networks. Cheng Zhang and Yingzhen Li 30- Improving PAC-Bayes bounds for neural networks using geometric properties of the training method. Anirbit Mukherjee, Dan Roy, Pushpendre Rastogi and Jun Yang 31- An Analysis of the Effect of Invariance on Generalization in Neural Networks. Clare Lyle, Marta Kwiatkowska and Yarin Gal 32- Data-Dependent Mututal Information Bounds for SGLD. Jeffrey Negrea, Daniel Roy, Gintare Karolina Dziugaite, Mahdi Haghifam and Ashish Khisti 33- Comparing normalization in conditional computation tasks. Vincent Michalski, Vikram Voleti, Samira Ebrahimi Kahou, Anthony Ortiz, Pascal Vincent, Chris Pal and Doina Precup 34- Weight and Batch Normalization implement Classical Generalization Bounds. Andrzej Banburski, Qianli Liao, Brando Miranda, Lorenzo Rosasco, Jack Hidary and Tomaso Poggio 35- Increasing batch size through instance repetition improves generalization. Elad Hoffer, Tal Ben-Nun, Itay Hubara, Niv Giladi, Torsten Hoefler and Daniel Soudry 36- Zero-Shot Learning from scratch: leveraging local compositional representations. Tristan Sylvain, Linda Petrini and Devon Hjelm 37- Circuit-Based Intrinsic Methods to Detect Overfitting. Satrajit Chatterjee and Alan Mishchenko 38- Dimension Reduction Approach for Interpretability of Sequence to SequenceRecurrent Neural Networks. Kun Su and Eli Shlizerman 39- Tight PAC-Bayesian generalization error bounds for deep learning. Guillermo Valle Perez, Chico Q. Camargo and Ard A. Louis 40- How Learning Rate and Delay Affect Minima Selection in AsynchronousTraining of Neural Networks: Toward Closing the Generalization Gap. Niv Giladi, Mor Shpigel Nacson, Elad Hoffer and Daniel Soudry 41- Making Convolutional Networks Shift-Invariant Again. Richard Zhang 42- A Meta-Analysis of Overfitting in Machine Learning. Sara Fridovich-Keil, Moritz Hardt, John Miller, Ben Recht, Rebecca Roelofs, Ludwig Schmidt and Vaishaal Shankar 43- Kernelized Capsule Networks. Taylor Killian, Justin Goodwin, Olivia Brown and Sung Son 44- Model similarity mitigates test set overuse. Moritz Hardt, Horia Mania, John Miller, Ben Recht and Ludwig Schmidt 45- Understanding Generalization of Deep Neural Networks Trained with Noisy Labels. Wei Hu, Zhiyuan Li and Dingli Yu 46- Domainwise Classification Network for Unsupervised Domain Adaptation. Seonguk Seo, Yumin Suh, Bohyung Han, Taeho Lee, Tackgeun You, Woong-Gi Chang and Suha Kwak 47- The Generalization-Stability Tradeoff in Neural Network Pruning. Brian Bartoldson, Ari Morcos, Adrian Barbu and Gordon Erlebacher 48- Information matrices and generalization. Valentin Thomas, Fabian Pedregosa, Bart van Merriënboer, Pierre-Antoine Manzagol, Yoshua Bengio and Nicolas Le Roux 49- Adaptively Preconditioned Stochastic Gradient Langevin Dynamics. Chandrasekaran Anirudh Bhardwaj 50- Additive or Concatenating Skip-connections Improve Data Separability. Yasutaka Furusho and Kazushi Ikeda 51- On the Inductive Bias of Neural Tangent Kernels. Alberto Bietti and Julien Mairal 52- PAC Bayes Bound Minimization via Kronecker Normalizing Flows. Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste and Aaron Courville 53- SGD on Neural Networks Learns Functions of Increasing Complexity. Preetum Nakkiran, Gal Kaplun, Dimitris Kalimeris, Tristan Yang, Ben Edelman, Fred Zhang and Boaz Barak 54- Overparameterization without Overfitting: Jacobian-based Generalization Guarantees for Neural Networks. Samet Oymak, Mingchen Li, Zalan Fabian and Mahdi Soltanolkotabi 55- Incorrect gradients and regularization: a perspective of loss landscapes. Mehrdad Yazdani 56- Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks. Ahmed Youssef, Prannoy Pilligundla and Hadi Esmaeilzadeh 57- SinReQ: Generalized Sinusoidal Regularization for Low-Bitwidth Deep Quantized Training. Ahmed Youssef, Prannoy Pilligundla and Hadi Esmaeilzadeh 58- Natural Adversarial Examples. Dan Hendrycks, Kevin Zhao, Steven Basart, Jacob Steinhardt and Dawn Song 59- On the Properties of the Objective Landscapes and Generalization of Gradient-Based Meta-Learning. Simon Guiroy, Vikas Verma and Christopher Pal 60- Angular Visual Hardness. Beidi Chen, Weiyang Liu, Animesh Garg, Zhiding Yu, Anshumali Shrivastava and Animashree Anandkumar 61- Luck Matters: Understanding Training Dynamics of Deep ReLU Networks. Yuandong Tian, Tina Jiang, Qucheng Gong and Ari Morcos 62- Understanding of Generalization in Deep Learning via Tensor Methods. Jingling Li, Yanchao Sun, Ziyin Liu, Taiji Suzuki and Furong Huang 63- Learning from Rules Performs as Implicit Regularization. Hossein Hosseini, Ramin Moslemi, Ali Hooshmand and Ratnesh Sharma 64- Stochastic Mirror Descent on Overparameterized Nonlinear Models: Convergence, Implicit Regularization, and Generalization. Navid Azizan, Sahin Lale and Babak Hassibi 65- Scaling Characteristics of Sequential Multitask Learning: Networks Naturally Learn to Learn. Guy Davidson and Michael Mozer 66- Size-free generalization bounds for convolutional neural networks. Phillip Long and Hanie Sedghi