7 accepted papers
Quantum federated learning (QFL) is a quantum extension of the classical federated learning model across multiple local quantum devices. An efficient optimization algorithm is always expected to minimize the communication overhead among different quantum participants. In this work, we propose an eff…
This work investigates an extension of transfer learning applied in machine learning algorithms to the emerging hybrid end-to-end quantum neural network (QNN) for spoken command recognition (SCR). Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly…
This work aims to design a low complexity spoken command recognition (SCR) system by considering different trade-offs between the number of model parameters and classification accuracy. More specifically, we exploit a deep hybrid architecture of a tensor-train (TT) network to build an end-to-end SRC…
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models. This work studies the use of submodular functions to design a rank aggregation on score-based permutations, which can be used for distributed ASR systems in both superv…
Huge training datasets for automatic speech recognition (ASR) typically contain redundant information so that a subset of data is generally enough to obtain similar ASR performance to that obtained when the entire dataset is employed for training. Although the centralized submodular-based data selec…
This work is originated from the MLSP 2014 Classification Challenge which tries to automatically detect subjects with schizophrenia and schizo-affective disorder by analyzing multi-modal features derived from magnetic resonance imaging (MRI) data. We employ Deep Neural Network (DNN)-based multi-view…
Distributed deep neural networks are commonly employed for building automatic speech recognition (ASR) systems. In this work, we employ the robust submodular partitioning approach, which aims to split the training data into small disjoint data subsets and use each of these subsets to train a particu…