The categorical cross-entropy loss is shown for the convolutional neural network training as a function of the epoch number.</p
Cross entropy loss has served as the main objective function for classification-based tasks. Widely ...
A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification...
Deep learning techniques have become the tool of choice for side-channel analysis. In recent years, ...
In the process of machine learning, models are essentially defined by a group of parameters in multi...
Deep learning has been shown to achieve impressive results in several domains like computer vision a...
The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On t...
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective...
The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net train...
Deep learning has proven to be an important element of modern data processing technology, which has ...
The increasing size of modern datasets combined with the difficulty of obtaining real label informat...
Deep learning has proven to be an important element of modern data processing technology, which has ...
International audienceIn this paper, we propose to quantitatively compare loss functions based on pa...
In this supplementary material, we present the details of the neural network architecture and traini...
While cross entropy (CE) is the most commonly used loss to train deep neural networks for classifica...
Recent researches reveal that deep neural networks are sensitive to label noises hence leading to po...
Cross entropy loss has served as the main objective function for classification-based tasks. Widely ...
A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification...
Deep learning techniques have become the tool of choice for side-channel analysis. In recent years, ...
In the process of machine learning, models are essentially defined by a group of parameters in multi...
Deep learning has been shown to achieve impressive results in several domains like computer vision a...
The cross-entropy softmax loss is the primary loss function used to train deep neural networks. On t...
Recently, there is a growing interest in applying Transfer Entropy (TE) in quantifying the effective...
The recently discovered Neural Collapse (NC) phenomenon occurs pervasively in today's deep net train...
Deep learning has proven to be an important element of modern data processing technology, which has ...
The increasing size of modern datasets combined with the difficulty of obtaining real label informat...
Deep learning has proven to be an important element of modern data processing technology, which has ...
International audienceIn this paper, we propose to quantitatively compare loss functions based on pa...
In this supplementary material, we present the details of the neural network architecture and traini...
While cross entropy (CE) is the most commonly used loss to train deep neural networks for classifica...
Recent researches reveal that deep neural networks are sensitive to label noises hence leading to po...
Cross entropy loss has served as the main objective function for classification-based tasks. Widely ...
A large amount of research on Convolutional Neural Networks (CNN) has focused on flat Classification...
Deep learning techniques have become the tool of choice for side-channel analysis. In recent years, ...