Weight uncertainty in neural networks. In Proceedings of The 32nd International Conference on Machine Learning, pp. 1613-1622, 2015.Charles Blundell, Julien Cornebise, Koray Kavukcuoglu, and Daan Wierstra. Weight uncertainty in neural network. In ICML, 2015....
WeightUncertaintyinNeural Networks CharlesBlundell,JulienCornebise,KorayKavukcuoglu, DaanWierstra PresentedbyMichaelCogswell PointEstimatesofNetworkWeights MLE PointEstimatesofNeuralNetworks MAP ADistributionoverNeuralNetworks IdealTestDistribution Approximate 1. 2. Why? 1.Regularization 2.Understandnetworkuncertainty...
Hyperspherical Weight Uncertainty in Neural Networks Bayesian neural networks learn a posterior probability distribution over the weights of the network to estimate the uncertainty in predictions. Parameteriz... B Ghoshal,A Tucker 被引量: 0发表: 2021年 Implicit Weight Uncertainty in Neural Networks Mode...
We then demonstrate how the learnt uncertainty in the weights can be used to improve generalisation in non-linear regression problems, and how this weight uncertainty can be used to drive the exploration-exploitation trade-off in reinforcement learning....
Surrogate model development is a critical step for uncertainty quantification or other sample-intensive tasks for complex computational models. In this work we develop a multi-output surrogate form using a class of neural networks (NNs) that employ shortcut connections, namely Residual NNs (ResNets)...
18 proposed a data-driven approach to quantify the prediction uncertainty of deep neural networks (DNNs), paving the way for a comprehensive treatment of uncertainty in DNN-based diagnostic systems. Saxena et al.19 applied an advanced convolutional neural network model for early detection of DR to...
This paper analyzes the robustness of global exponential stability of stochastic recurrent neural networks (SRNNs) subject to parameter uncertainty in connection weight matrices. Given a globally exponentially stable stochastic recurrent neural network, the problem to be addressed here is how much parameter...
In the absence of the weight programming optimisation approach introduced in this paper, this uncertainty makes it very virtually impossible to evaluate—analytically or through intuition—the true inference potential from a given set of device characteristics. Our weight programming optimisation approach ...
In: CVPR. 2017. p. 2261–269. Li L, Talwalkar A. Random search and reproducibility for neural architecture search. In: Proceedings of the Thirty-Fifth Conference on Uncertainty in Artificial Intelligence, UAI, Tel Aviv, Israel, July 22–25. AUAI Press; 2019. p. 367–77. Real E, ...
Under this paradigm, the epistemic uncertainty is described by the weight distribution of maximal entropy that produces neural networks "consistent" with the training observations. Considering stochastic neural networks, a practical optimization is derived to build such a distribution, defined as a trade...