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Abstract: We present an efficient Bayesian convolutional neural network (convnet) The model offers better robustness to overfitting on small data than  - Information from 128.84.21.199

We consider the blackbox adversarial setting where the adversary has to generate adversarial perturbations without access to the target models  - Information from 128.84.21.199

of the eigenvectors associated to the largest eigenvalues and so can be used to do principal component analysis on quantum computers - Information from 128.84.21.199

errors to detect anomalies We propose a Long Short Term Memory Networks Anomaly Detection in multisensor timeseries (EncDec - Information from 128.84.21.199

Abstract: Quantum optical Gaussian states are a type of important robust quantum states which are manipulatable by the existing technologies  - Information from 128.84.21.199

layers into convolutional neural network (CNN) to introduce context words then gets the better sentence representation to classify the sentence - Information from 128.84.21.199

Specifically we propose a novel dual contrastive loss and show that with this loss discriminator learns more generalized and  - Information from 128.84.21.199

binations of chemical elements and interatomic distances based representation called Smooth Overlap of Atomic Positions (SOAP) - Information from 128.84.21.199

Because data remains local to each compute node federated learning is loss to train deep image segmentation networks for better topological accuracy - Information from 128.84.21.199

The second graph regularized neural networks leverages graphs to augment neural network losses with a regularization objective for semi  - Information from 128.84.21.199

concentration For electrostatically gated monolayer MoS2 and WS2 giant bandgap renormalization dominates the reduction of exciton binding energy and - Information from 128.84.21.199

To achieve higher accuracy some authors have used very deep convolutional neural networks For example Conneau et al [17] presented very deep CNNs for text - Information from 128.84.21.199

the JordanWigner transformation a deformed Fourier transform (generalizing the The inverse formula reads - Information from 128.84.21.199