Fetching the paper…
Reading the bibliography…
Reinforcement Learning (RL) based solutions are being adopted in a variety of domains including robotics, health care and industrial automation.
Verification of forecasts expressed in terms of probability
G. W. Brier · 1950
Earlier work this paper cites.
Newsweeder: Learning to filter netnews
Ken Lang · 1995
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L. Li, Kai Li, and Li Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky · 2012
Earlier work this paper cites.
Dropout as a bayesian approximation: Representing model uncertainty in deep learning
Yarin Gal and Zoubin Ghahramani · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
What is the difference between dropout and drop connect?
Matt Krause (https://stats.stackexchange.com/users/7250/matt krause) · 2016
Earlier work this paper cites.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2016
Earlier work this paper cites.
Finding anomalies with generative adversarial networks for a patrolbot
W. Lawson, E. Bekele, and K. Sullivan · 2017
Earlier work this paper cites.
Principled detection of out-of-distribution examples in neural networks
Shiyu Liang, Yixuan Li, and R. Srikant · 2017
Earlier work this paper cites.
Training confidence-calibrated classifiers for detecting out-of-distribution samples, 2018
Kimin Lee, Honglak Lee, Kibok Lee, and Jinwoo Shin · 2018
Cited alongside, same era.
Open category detection with PAC guarantees
Si Liu, Risheek Garrepalli, Thomas G. Dietterich, Alan Fern, and Dan Hendrycks · 2018
Cited alongside, same era.
Classification uncertainty of deep neural networks based on gradient information
Philipp Oberdiek, Matthias Rottmann, and Hanno Gottschalk · 2018
Cited alongside, same era.
A general reinforcement learning algorithm that masters chess, shogi, and go through self-play
David Silver, Thomas Hubert, Julian Schrittwieser, Ioannis Antonoglou, Matthew Lai, Arthur Guez, Marc Lanctot, Laurent Sifre, Dharshan Kumaran, Thore Graepel, et al · 2018
Cited alongside, same era.
Flipout: Efficient pseudo-independent weight perturbations on mini-batches
Likelihood ratios for out-of-distribution detection, 2019
Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, and Balaji Lakshminarayanan · 2019
Later among the works it cites.
Uncertainty-based out-of-distribution detection in deep reinforcement learning
Andreas Sedlmeier, Thomas Gabor, Thomy Phan, Lenz Belzner, and Claudia Linnhoff-Popien · 2019
Later among the works it cites.
Unsupervised out-of-distribution detection by maximum classifier discrepancy, 2019
Qing Yu and Kiyoharu Aizawa · 2019
Later among the works it cites.
A dynamic ensemble learning algorithm for neural networks
K.M.R. Alam, N. Siddique, and H Adeli · 2020
Later among the works it cites.
Anomalous example detection in deep learning: A survey
S. Bulusu, B. Kailkhura, B. Li, P. K. Varshney, and D. Song · 2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Yeming Wen, Paul Vicol, Jimmy Ba, Dustin Tran, and Roger Grosse · 2018
Cited alongside, same era.
Adversarial examples detection in features distance spaces
Fabio Carrara, Rudy Becarelli, Roberto Caldelli, Fabrizio Falchi, and Giuseppe Amato · 2019
Cited alongside, same era.
Learning agile and dynamic motor skills for legged robots
Jemin Hwangbo, Joonho Lee, Alexey Dosovitskiy, Dario Bellicoso, Vassilios Tsounis, Vladlen Koltun, and Marco Hutter · 2019
Cited alongside, same era.
Safe reinforcement learning with model uncertainty estimates
Björn Lütjens, Michael Everett, and Jonathan P. How · 2019
Cited alongside, same era.
Benchmarking robustness in object detection: Autonomous driving when winter is coming
Claudio Michaelis, Benjamin Mitzkus, Robert Geirhos, Evgenia Rusak, Oliver Bringmann, Alexander S. Ecker, Matthias Bethge, and Wieland Brendel · 2019
Cited alongside, same era.
Can you trust your model’s uncertainty? evaluating predictive uncertainty under dataset shift, 2019
Yaniv Ovadia, Emily Fertig, Jie Ren, Zachary Nado, D Sculley, Sebastian Nowozin, Joshua V. Dillon, Balaji Lakshminarayanan, and Jasper Snoek · 2019
Cited alongside, same era.
The many faces of robustness: A critical analysis of out-of-distribution generalization
Dan Hendrycks, Steven Basart, Norman Mu, Saurav Kadavath, Frank Wang, Evan Dorundo, Rahul Desai, Tyler Zhu, Samyak Parajuli, Mike Guo, et al · 2020
Later among the works it cites.
Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2021
Closest in time.
A reinforcement learning approach to irrigation decision-making for rice using weather forecasts
Mengting Chen, Yuanlai Cui, Xiaonan Wang, Hengwang Xie, Fangping Liu, Tongyuan Luo, Shizong Zheng, and Yufeng Luo · 2021
Closest in time.
Dropconnect is effective in modeling uncertainty of bayesian deep networks
Aryan Mobiny, Pengyu Yuan, Supratik K Moulik, Naveen Garg, Carol C Wu, and Hien Van Nguyen · 2021
Closest in time.