Search Results for author:
Found 8 papers, 3 papers with code
Optimizing Quantum Federated Learning Based on Federated Quantum Natural Gradient Descent
In this work, we propose an efficient optimization algorithm, namely federated quantum natural gradient descent (FQNGD), and further, apply it to a QFL framework that is composed of a variational quantum circuit (VQC)-based quantum neural networks (QNN).
Exploiting Low-Rank Tensor-Train Deep Neural Networks Based on Riemannian Gradient Descent With Illustrations of Speech Processing
This work focuses on designing low complexity hybrid tensor networks by considering trade-offs between the model complexity and practical performance.
Exploiting Hybrid Models of Tensor-Train Networks for Spoken Command Recognition
Our command recognition system, namely CNN+(TT-DNN), is composed of convolutional layers at the bottom for spectral feature extraction and TT layers at the top for command classification.
Classical-to-Quantum Transfer Learning for Spoken Command Recognition Based on Quantum Neural Networks
Our QNN-based SCR system is composed of classical and quantum components: (1) the classical part mainly relies on a 1D convolutional neural network (CNN) to extract speech features; (2) the quantum part is built upon the variational quantum circuit with a few learnable parameters.
Variational Inference-Based Dropout in Recurrent Neural Networks for Slot Filling in Spoken Language Understanding
This paper proposes to generalize the variational recurrent neural network (RNN) with variational inference (VI)-based dropout regularization employed for the long short-term memory (LSTM) cells to more advanced RNN architectures like gated recurrent unit (GRU) and bi-directional LSTM/GRU.
Submodular Rank Aggregation on Score-based Permutations for Distributed Automatic Speech Recognition
Distributed automatic speech recognition (ASR) requires to aggregate outputs of distributed deep neural network (DNN)-based models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Riemannian Stochastic Gradient Descent for Tensor-Train Recurrent Neural Networks
The Tensor-Train factorization (TTF) is an efficient way to compress large weight matrices of fully-connected layers and recurrent layers in recurrent neural networks (RNNs).
Unsupervised Submodular Rank Aggregation on Score-based Permutations
Unsupervised rank aggregation on score-based permutations, which is widely used in many applications, has not been deeply explored yet.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
