Accepted Papers

  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
  67. Demystification of Flat Minima and Generalisability of Deep Neural Networks. Hao Shen and Martin Gottwald.