Accepted Papers
Note: Papers listed here do not constitute workshop proceedings.
- Why ReLU networks yield high-confidence predictions far away from the training data and how to mitigate the problem Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf
- Subspace Inference for Bayesian Deep Learning Wesley Maddox, Timur Garipov, Pavel Izmailov, Polina Kirichenko, Dmitry Vetrov, Andrew Gordon Wilson
- ‘In-Between’ Uncertainty in Bayesian Neural Networks Andrew Y. K. Foong, Yingzhen Li, Jose Miguel Hernandez-Lobato, Richard E. Turner
- Quality of Uncertainty Quantification for Bayesian Neural Network Inference Jiayu Yao, Weiwei Pan, Soumya Ghosh, Finale Doshi-Velez
- Detecting Extrapolation with Influence Functions David Madras, James Atwood, Alex D'Amour
- How Can We Be So Dense? The Robustness of Highly Sparse Representations Subutai Ahmad, Luiz Scheinkman
- Likelihood Ratios for Out-of-Distribution Detection Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan
- Improving anomaly detection with differential privacy Min Du, Dawn Song
- Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift Yaniv Ovadia, Emily Fertig, Jie Ren, Zack Nado, D. Sculley, Sebastian Nowozin, Josh Dillon, Balaji Lakshminarayanan, Jasper Snoek
- RobULA: Efficient Sampling for Robust Bayesian Inference Kush Bhatia, Yi-An Ma, Peter L. Bartlett, Anca D. Dragan, Michael I. Jordan
- Out-of-Sample Robustness for Neural Networks via Confidence Densities Robert Cornish, George Deligiannidis, Arnaud Doucet
- Uncertainty estimates and out-of-distribution detection with Sine Networks Hartmut Maennel
- Efficient evaluation-time uncertainty estimation by improved distillation Erik Englesson, Hossein Azizpour
- Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels Bo Han, Gang Niu, Jiangchao Yao, Xingrui Yu, Miao Xu, Ivor W. Tsang, Masashi Sugiyama
- Deep Support Vector Data Description for Unsupervised and Semi-Supervised Anomaly Detection Lukas Ruff, Robert A. Vandermeulen, Nico Görnitz, Alexander Binder, Emmanuel Müller, and Marius Kloft
- Affine Variational Autoencoders: An Efficient Approach for Improving Generalization and Robustness to Distribution Shift Rene Bidart, Alexander Wong
- Exploring Deep Anomaly Detection Methods Based on Capsule Net Xiaoyan Li, Iluju Kiringa, Tet Yeap, Xiaodan Zhu, Yifeng Li
- Output-Constrained Bayesian Neural Networks Wanqian Yang*, Lars Lorch*, Moritz A. Graule*, Srivatsan Srinivasan, Anirudh Suresh, Jiayu Yao, Melanie F. Pradier, Finale Doshi-Velez
- Defense Against Adversarial Attacks by Langevin Dynamics Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima
- CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks Alberto Marchisio, Giorgio Nanfa, Faiq Khalid, Muhammad Abdullah Hanif, Maurizio Martina, Muhammad Shafique
- An Empirical Evaluation on Robustness and Uncertainty of Regularization Methods Sanghyuk Chun, Seong Joon Oh, Sangdoo Yun, Dongyoon Han, Junsuk Choe, Youngjoon Yoo
- Transfer of Adversarial Robustness Between Perturbation Types Daniel Kang*, Yi Sun*, Tom Brown, Dan Hendrycks, Jacob Steinhardt
- On Norm-Agnostic Robustness of Adversarial Training for MNIST Bai Li, Changyou Chen, Wenlin Wang, Lawrence Carin
- Mitigating Model Non-Identifiability in BNN with Latent Variables Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
- Learning for Single-Shot Confidence Calibration in Deep Nerual Networks through Stochastic Inferences Seonguk Seo, Paul Hongsuck Seo, Bohyung Han
- Disentangling Adversarial Robustness and Generalization David Stutz, Matthias Hein, Bernt Schiele
- Detecting Adversarial Examples and Other Misclassifications in Neural Networks by Introspection Jonathan Aigrain, Marcin Detyniecki
- Unsupervised Temperature Scaling: Post-Processing Unsupervised Calibration of Deep Models Decisions Azadeh Sadat Mozafari, Hugo Siqueira Gomes, Wilson Leão, Christian Gagné
- Calibration of Encoder Decoder Models for Neural Machine Translation Aviral Kumar, Sunita Sarawagi
- Using learned optimizers to make models robust to input noise Luke Metz, Niru Maheswaranathan, Jonathon Shlens, Jascha Sohl-Dickstein, Ekin D. Cubuk
- Implicit Generative Modeling of Random Noise during Training improves Adversarial Robustness Priyadarshini Panda, Kaushik Roy
- Leverage Temporal Consistency for Robust Semantic Video Segmentation Timo Sämann, Karl Amende, Stefan Milz, Hort Michael Gross
- EnsembleDAgger: A Bayesian Approach to Safe Imitation Learning Kunal Menda, Katherine Driggs-Cambell, Mykel J. Kochenderfer
- Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation Raphael Gontijo Lopes, Dong Yin, Ben Poole, Justin Gilmer, Ekin D. Cubuk
- Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness Saeed Mahloujifar, Xiao Zhang, Mohammad Mahmoody, David Evans
- MNIST-C: A Robustness Benchmark for Computer Vision Norman Mu, Justin Gilmer
- VAE-GANs for Compressive Medical Image Recovery: Uncertainty Analysis Vineet Edupuganti, Morteza Mardani, Joseph Cheng, Shreyas Vasanawala, John Pauly
- A Fourier Perspective on Model Robustness in Computer Vision Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer
- Robust conditional GANs under missing or uncertain labels Kiran Koshy Thekumparampil, Sewoong Oh, Ashish Khetan
- Defending Deep Neural Networks against Structural Perturbations Uttaran Sinha, Dr Saurabh Joshi, Dr Vineeth N Balasubramanian
- Analyzing the Role of Model Uncertainty for Electronic Health Records Michael W. Dusenberry, Andrew Dai, Dustin Tran, Edward Choi, Jonas Kemp, Jeremy Nixon, Ghassen Jerfel, Katherine Heller
- On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks Sunil Thulasidasan, Gopinath Chennupati, Jeffrey Bilmes, Sarah Michalak, Tanmoy Bhattacharya
- Understanding Adversarial Robustness Through Loss Landscape Geometries Joyce Xu, Dian Ang Yap, Vinay Uday Prabhu
- Learning a Hierarchy of Neural Connections for Modeling Uncertainty Raanan Y. Rohekar, Yaniv Gurwicz, Shami Nisimov, Gal Novik
- Bayesian Evaluation of Black-Box Classifiers Disi Ji, Robert Logan, Padhraic Smyth, Mark Steyvers
- Stochastic Prototype Embeddings Tyler R. Scott, Michael C. Mozer
- Assessing the Robustness of Bayesian Dark Knowledge to Posterior Uncertainty Meet P. Vadera, Benjamin M. Marlin
- Uncertainty Quantification for Deep Context-Aware Mobile Activity Recognition and Unknown Context Discovery Aven Samareh, Arash Pakbin, Xiaohan Chen, Nathan C. Hurley, Ye Yuan, Xiaoning Qian, Zhangyang Wang, Shuai Huang, Bobak Mortazavi
- Modeling Assumptions and Evaluation Schemes: On the Assessment of Deep Latent Variable Models Judith, Bütepage, Petra Poklukar, Danica Kragic
- Continual Learning by Kalman Optimiser Honglin Li, Shirin Enshaeifar, Frieder Ganz, Payam Barnaghi
- Predicting Model Failure using Saliency Maps in Autonomous Driving Systems Sina Mohseni, Akshay Jagadeesh, Zhangyang Wang
- Deeper Connections between Neural Networks and Gaussian Processes Speed-up Active Learning Evgenii Tsymbalov, Sergei Makarychev, Alexander Shapeev, Maxim Panov