Open AccessProceedings Article
Dropout as a Bayesian approximation: representing model uncertainty in deep learning
Yarin Gal,Zoubin Ghahramani +1 more
- pp 1050-1059
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TLDR
A new theoretical framework is developed casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes, which mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy.Abstract:
Deep learning tools have gained tremendous attention in applied machine learning. However such tools for regression and classification do not capture model uncertainty. In comparison, Bayesian models offer a mathematically grounded framework to reason about model uncertainty, but usually come with a prohibitive computational cost. In this paper we develop a new theoretical framework casting dropout training in deep neural networks (NNs) as approximate Bayesian inference in deep Gaussian processes. A direct result of this theory gives us tools to model uncertainty with dropout NNs - extracting information from existing models that has been thrown away so far. This mitigates the problem of representing uncertainty in deep learning without sacrificing either computational complexity or test accuracy. We perform an extensive study of the properties of dropout's uncertainty. Various network architectures and nonlinearities are assessed on tasks of regression and classification, using MNIST as an example. We show a considerable improvement in predictive log-likelihood and RMSE compared to existing state-of-the-art methods, and finish by using dropout's uncertainty in deep reinforcement learning.read more
Citations
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Adaptive Prior Selection for Repertoire-based Online Adaptation in Robotics
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Towards More Accurate Uncertainty Estimation In Text Classification
Jianfeng He,Xuchao Zhang,Shuo Lei,Zhiqian Chen,Fanglan Chen,Abdulaziz Alhamadani,Bei Xiao,Chang-Tien Lu +7 more
TL;DR: A model called MSD is proposed to improve the accuracy of uncertainty score by reducing the effect of overconfidence of winning score and considering the impact of different categories of uncertainty simultaneously, which can be applied with different Deep Neural Networks.
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Can you trust predictive uncertainty under real dataset shifts in digital pathology
Jeppe Thagaard,Søren Hauberg,Bert van der Vegt,Thomas Ebstrup,Johan Dore Hansen,Anders Bjorholm Dahl +5 more
TL;DR: It is demonstrated that current methods for uncertainty quantification are not necessarily able to detect all dataset shifts, and the importance of monitoring and controlling the input distribution when deploying deep learning for digital pathology is emphasized.
Approximating the Predictive Distribution via Adversarially-Trained Hypernetworks
Christian Henning,Johannes von Oswald,João Sacramento,Simone Carlo Surace,Jean-Pascal Pfister,Benjamin F. Grewe +5 more
TL;DR: This work defines a weight posterior to uniformly allow weight realizations of a neural network that meet a chosen fidelity constraint and trains a combination of hypernetwork and main network via the GAN framework by sampling from this posterior predictive distribution.
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References
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TL;DR: This work bridges the divide between high-dimensional sensory inputs and actions, resulting in the first artificial agent that is capable of learning to excel at a diverse array of challenging tasks.
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Diederik P. Kingma,Max Welling +1 more
TL;DR: A stochastic variational inference and learning algorithm that scales to large datasets and, under some mild differentiability conditions, even works in the intractable case is introduced.