Fetching the paper…
Reading the bibliography…
Capturing aleatoric uncertainty is a critical part of many machine learning systems.
Estimating the mean and variance of the target probability distribution
David A. Nix and Andreas S. Weigend · 1994
Earlier work this paper cites.
Aleatory or epistemic? does it matter?
Armen Der Kiureghian and Ove Ditlevsen · 2008
Earlier work this paper cites.
Indoor segmentation and support inference from RGBD images
Nathan Silberman, Derek Hoiem, Pushmeet Kohli, and Rob Fergus · 2012
Earlier work this paper cites.
Auto-encoding variational Bayes
Diederik P. Kingma and Max Welling · 2014
Earlier work this paper cites.
Probabilistic backpropagation for scalable learning of Bayesian neural networks
José Miguel Hernández-Lobato and Ryan P. Adams · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Concrete problems in AI safety
Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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.
Deep exploration via bootstrapped DQN
Ian Osband, Charles Blundell, Alexander Pritzel, and Benjamin Van Roy · 2016
Cited alongside, same era.
Deep Bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
Cited alongside, same era.
What uncertainties do we need in Bayesian deep learning for computer vision?
Alex Kendall and Yarin Gal · 2017
Cited alongside, same era.
Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
Cited alongside, same era.
Deep reinforcement learning in a handful of trials using probabilistic dynamics models
Kurtland Chua, Roberto Calandra, Rowan McAllister, and Sergey Levine · 2018
Cited alongside, same era.
Implicit quantile networks for distributional reinforcement learning
Will Dabney, Georg Ostrovski, David Silver, and Rémi Munos · 2018
Reliable training and estimation of variance networks
Nicki S. Detlefsen, Martin Jørgensen, and Søren Hauberg · 2019
Later among the works it cites.
From big to small: Multi-scale local planar guidance for monocular depth estimation
Jin Han Lee, Myung-Kyu Han, Dong Wook Ko, and Il Hong Suh · 2019
Later among the works it cites.
Variational variance: Simple and reliable predictive variance parameterization
Andrew Stirn and David A. Knowles · 2020
Later among the works it cites.
MOPO: Model-based offline policy optimization
Tianhe Yu, Garrett Thomas, Lantao Yu, Stefano Ermon, James Y. Zou, Sergey Levine, Chelsea Finn, and Tengyu Ma · 2020
Later among the works it cites.
AdaBins: Depth estimation using adaptive bins
Shariq Farooq Bhat, Ibraheem Alhashim, and Peter Wonka · 2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Multi-goal reinforcement learning: Challenging robotics environments and request for research
Matthias Plappert, Marcin Andrychowicz, Alex Ray, Bob McGrew, Bowen Baker, Glenn Powell, Jonas Schneider, Josh Tobin, Maciek Chociej, Peter Welinder, Vikash Kumar, and Wojciech Zaremba · 2018
Cited alongside, same era.
Student-t variational autoencoder for robust density estimation
Hiroshi Takahashi, Tomoharu Iwata, Yuki Yamanaka, Masanori Yamada, and Satoshi Yagi · 2018
Cited alongside, same era.
Structure-aware residual pyramid network for monocular depth estimation
Xiaotian Chen, Xuejin Chen, and Zheng-Jun Zha · 2019
Cited alongside, same era.
Estimating and evaluating regression predictive uncertainty in deep object detectors
Ali Harakeh and Steven L. Waslander · 2021
Later among the works it cites.
Extracting strong policies for robotics tasks from zero-order trajectory optimizers
Cristina Pinneri, Shambhuraj Sawant, Sebastian Blaes, and Georg Martius · 2021
Later among the works it cites.
Causal influence detection for improving efficiency in reinforcement learning
Maximilian Seitzer, Bernhard Schölkopf, and Georg Martius · 2021
Later among the works it cites.
Risk-averse zero-order trajectory optimization
Marin Vlastelica, Sebastian Blaes, Cristina Pinneri, and Georg Martius · 2021
Later among the works it cites.