Exploració per autor "Ortega, Antonio"
Ara es mostren els items 1-5 de 5
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Channel redundancy and overlap in convolutional neural networks with channel-wise NNK graphs
Bonet Solé, David; Ortega, Antonio; Ruiz Hidalgo, Javier; Shekkizhar, Sarath (Institute of Electrical and Electronics Engineers (IEEE), 2022)
Text en actes de congrés
Accés obertFeature spaces in the deep layers of convolutional neural networks (CNNs) are often very high-dimensional and difficult to inter-pret. However, convolutional layers consist of multiple channels that are activated by different ... -
Channel-wise early stopping without a validation set via NNK polytope interpolation
Bonet Solé, David; Ortega, Antonio; Ruiz Hidalgo, Javier; Sarath Shekkizhar, Sarath (2021)
Text en actes de congrés
Accés obertState-of-the-art neural network architectures continue to scale in size and deliver impressive generalization results, although this comes at the expense of limited interpretability. In particular, a key challenge is to ... -
Modeling of contours in wavelet domain for generalized lifting image compression
Rolon Garrido, Julio Cesar; Ortega, Antonio; Salembier Clairon, Philippe Jean (2009)
Text en actes de congrés
Accés obertThis paper introduces the design of context-based models of contours in the wavelet domain, which are used to construct generalized lifting (GL) mappings for image compression. The GL context-based mapping may significantly ... -
Performance evaluation of probability density estimators for unsupervised information theoretical region merging
Calderero Patino, Felipe; Marqués Acosta, Fernando; Ortega, Antonio (2009)
Comunicació de congrés
Accés obertInformation theoretical region merging techniques have been shown to provide a state-of-the-art unified solution for natural and texture image segmentation. Here, we study how the segmentation results can be further ... -
Study of manifold geometry using multiscale non-negative kernel graphs
Hurtado Gómez, Carlos; Shekkizhar, Sarath; Ruiz Hidalgo, Javier; Ortega, Antonio (Institute of Electrical and Electronics Engineers (IEEE), 2023)
Text en actes de congrés
Accés obertModern machine learning systems are increasingly trained on large amounts of data embedded in high-dimensional spaces. Often this is done without analyzing the structure of the dataset. In this work, we propose a framework ...