scholarly journals Deep Confidence: A Computationally Efficient Framework for Calculating Reliable Prediction Errors for Deep Neural Networks

2018 ◽  
Vol 59 (3) ◽  
pp. 1269-1281 ◽  
Author(s):  
Isidro Cortés-Ciriano ◽  
Andreas Bender
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Prediction Errors ◽  
Computationally Efficient ◽  
Reliable Prediction

scholarly journals A Bayesian graph convolutional network for reliable prediction of molecular properties with uncertainty quantification

2019 ◽  
Vol 10 (36) ◽  
pp. 8438-8446 ◽  
Author(s):  
Seongok Ryu ◽  
Yongchan Kwon ◽  
Woo Youn Kim
Keyword(s):  
Neural Networks ◽  
Bayesian Inference ◽  
Uncertainty Analysis ◽  
Uncertainty Quantification ◽  
Deep Neural Networks ◽  
Molecular Properties ◽  
Reliable Prediction ◽  
Convolutional Network ◽  
Chemical Fields

Deep neural networks have been increasingly used in various chemical fields. Here, we show that Bayesian inference enables more reliable prediction with quantitative uncertainty analysis.

scholarly journals Dropout with Tabu Strategy for Regularizing Deep Neural Networks

2019 ◽  
Vol 63 (7) ◽  
pp. 1031-1038
Author(s):  
Zongjie Ma ◽  
Abdul Sattar ◽  
Jun Zhou ◽  
Qingliang Chen ◽  
Kaile Su
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Networks ◽  
Network Architectures ◽  
Computationally Efficient ◽  
Diversification Strategy ◽  
Working Mechanism ◽  
Training Stage ◽  
Neural Network Architectures ◽  
Public Datasets

Abstract Dropout has been proven to be an effective technique for regularizing and preventing the co-adaptation of neurons in deep neural networks (DNN). It randomly drops units with a probability of p during the training stage of DNN to avoid overfitting. The working mechanism of dropout can be interpreted as approximately and exponentially combining many different neural network architectures efficiently, leading to a powerful ensemble. In this work, we propose a novel diversification strategy for dropout, which aims at generating more different neural network architectures in less numbers of iterations. The dropped units in the last forward propagation will be marked. Then the selected units for dropping in the current forward propagation will be retained if they have been marked in the last forward propagation, i.e., we only mark the units from the last forward propagation. We call this new regularization scheme Tabu dropout, whose significance lies in that it does not have extra parameters compared with the standard dropout strategy and is computationally efficient as well. Experiments conducted on four public datasets show that Tabu dropout improves the performance of the standard dropout, yielding better generalization capability.

Deep neural networks trained with heavier data augmentation learn features closer to representations in hIT

2018 ◽  
Author(s):  
Alex Hernández-García ◽  
Johannes Mehrer ◽  
Nikolaus Kriegeskorte ◽  
Peter König ◽  
Tim C. Kietzmann
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Data Augmentation

Representation of adversarial images in deep neural networks and the human brain

2018 ◽  
Author(s):  
Chi Zhang ◽  
Xiaohan Duan ◽  
Ruyuan Zhang ◽  
Li Tong
Keyword(s):  
Neural Networks ◽  
Human Brain ◽  
Deep Neural Networks
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