Complex-valued neural networks (CVNNs) have shown their excellent efficiency compared
to their real counter-parts in speech enhancement, image and signal processing …

Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

Abstract Recurrent Neural Networks (RNNs) offer fast inference on long sequences but are
hard to optimize and slow to train. Deep state-space models (SSMs) have recently been …

Hybrid quantum–classical systems make it possible to utilize existing quantum computers to
their fullest extent. Within this framework, parameterized quantum circuits can be regarded …

For most deep learning practitioners, sequence modeling is synonymous with recurrent
networks. Yet recent results indicate that convolutional architectures can outperform …

What does a neural network encode about a concept as we traverse through the layers?
Interpretability in machine learning is undoubtedly important, but the calculations of neural …

Recurrent neural networks (RNNs) have been widely used for processing sequential data.
However, RNNs are commonly difficult to train due to the well-known gradient vanishing and …

Recurrent neural networks (RNNs), such as long short-term memory networks (LSTMs),
serve as a fundamental building block for many sequence learning tasks, including machine …

At present, the vast majority of building blocks, techniques, and architectures for deep
learning are based on real-valued operations and representations. However, recent work on …

Most deep learning-based models for speech enhancement have mainly focused on
estimating the magnitude of spectrogram while reusing the phase from noisy speech for …