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Deep Representation Learning for Trigger Monitoring

We propose a novel neural network architecture called Hierarchical Latent Autoencoder to exploit the underlying hierarchical nature of the CMS Trigger System at CERN for data quality monitoring. The results demonstrate that our architecture does reducehe reconstruction error on the test set from $9.35 \times 10^{-6}$ when using a vanilla Variational Autoencoder to $4.52 \times 10^{-6}$ when using our Hierarchical Latent Autoencoder.
CERN Openlab Technical Report, 2018

Text Normalization using Memory Augmented Neural Networks

With the addition of dynamic memory access and storage mechanism, we present a neural architecture that will serve as a language-agnostic text normalization system while avoiding the kind of unacceptable errors made by the LSTM based recurrent neural networks. Our proposed system requires significantly lesser amounts of data, training time and compute resources.
Speech Communication (EURASIP & ISCA) Elsevier, 2018