Although complex-valued convolutional neural networks (iCNNs) have existed for a while,
they lack proper complex-valued image inputs and loss functions. In addition, all their …

Nesterov accelerated gradient (NAG) method is an efficient first-order algorithm for
optimization problems. To ensure the convergence, it usually takes a relatively conservative …

Multi-spectral imagery is invaluable for remote sensing due to different spectral signatures
exhibited by materials that often appear identical in greyscale and RGB imagery. Paired with …

The vast majority of existing neural networks operate by rules set within the algebra of real
numbers. However, as theoretical understanding of the fundamentals of neural networks …

Resource efficient and high precision compilation of programs into quantum circuits
expressed in Fault-Tolerant gate sets, such as the Clifford+ T gate set, is vital for the success …

Recent works on learned image compression (LIC) based on convolutional neural networks
(CNNs) have achieved great improvement with superior rate-distortion performance …

We explore the potential of contrastive learning for the classification of ships using real and
complex Synthetic Aperture Radar (SAR) modalities. We employ a network that takes SAR …

Hyper-spectral imaging (HSI) is a critical remote sensing modality that captures high-
resolution spectral information in addition to high spatial resolution. Due to its ability to …

Deep learning models operating in the complex domain are used due to their rich
representation capacity. However, most of these models are either restricted to the first …

Artificial neural networks have become an inseparable element of human life. Researches
do not stop at the current progress and try to improve neural networks and expand fields of …