title: Medios compositivos y nuevas formas de experiencia perceptiva en la poesía experimental española actual

authors: Lopez-Fernandez, Laura

abstract: La naturaleza híbrida y heterogénea de los modos poéticos experimentales —poéticas no versificadas y no declamatorias— constituye un rasgo distintivo de este macrogénero, el cual requiere para un mejor entendimiento un marco teórico apropiado y una revisión de las nociones convencionales de lírica, representación y discursividad. En este estudio se revisan estas nociones dentro del contexto poético español del siglo XXI y se presenta un nuevo modo de acercamiento a estas textualidades alteradas donde se subraya la no linealidad, los lenguajes no verbales y la tecnología como herramientas integrales en el proceso de creación y descodificación de estos múltiples modos compositivos.

text: Medios compositivos y nuevas formas de experiencia perceptiva en la poesía experimental española actual La naturaleza híbrida y heterogénea de los modos poéticos experimentales —poéticas no versificadas y no declamatorias— constituye un rasgo distintivo de este macrogénero, el cual requiere para un mejor entendimiento un marco teórico apropiado y una revisión de las nociones convencionales de lírica, representación y discursividad. En este estudio se revisan estas nociones dentro del contexto poético español del siglo XXI y se presenta un nuevo modo de acercamiento a estas textualidades alteradas donde se subraya la no linealidad, los lenguajes no verbales y la tecnología como herramientas integrales en el proceso de creación y descodificación de estos múltiples modos compositivos.

year: 2014

title: Transfer of pretrained model weights substantially improves semi-supervised image classification

authors: Sahito, Attaullah

subjects: computer science

abstract: Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires considerable resources, time, and effort. If labeling new data is not feasible, so-called semi-supervised learning can achieve better generalisation than purely supervised learning by employing unlabeled instances as well as labeled ones. The work presented in this paper is motivated by the observation that transfer learning provides the opportunity to potentially further improve performance by exploiting models pretrained on a similar domain. More specifically, we explore the use of transfer learning when performing semi-supervised learning using self-learning. The main contribution is an empirical evaluation of transfer learning using different combinations of similarity metric learning methods and label propagation algorithms in semi-supervised learning. We find that transfer learning always substantially improves the model’s accuracy when few labeled examples are available, regardless of the type of loss used for training the neural network. This finding is obtained by performing extensive experiments on the SVHN, CIFAR10, and Plant Village image classification datasets and applying pretrained weights from Imagenet for transfer learning.

text: Transfer of pretrained model weights substantially improves semi-supervised image classification Deep neural networks produce state-of-the-art results when trained on a large number of labeled examples but tend to overfit when small amounts of labeled examples are used for training. Creating a large number of labeled examples requires considerable resources, time, and effort. If labeling new data is not feasible, so-called semi-supervised learning can achieve better generalisation than purely supervised learning by employing unlabeled instances as well as labeled ones. The work presented in this paper is motivated by the observation that transfer learning provides the opportunity to potentially further improve performance by exploiting models pretrained on a similar domain. More specifically, we explore the use of transfer learning when performing semi-supervised learning using self-learning. The main contribution is an empirical evaluation of transfer learning using different combinations of similarity metric learning methods and label propagation algorithms in semi-supervised learning. We find that transfer learning always substantially improves the model’s accuracy when few labeled examples are available, regardless of the type of loss used for training the neural network. This finding is obtained by performing extensive experiments on the SVHN, CIFAR10, and Plant Village image classification datasets and applying pretrained weights from Imagenet for transfer learning.

year: 2020

title: Precariat Māori households today

authors: Rua, Mohi

abstract: Aotearoa New Zealand is now the fifth most unequal economy in the OECD. To highlight the human cost of this situation, the concept of “the precariat” offers more informed and contextualised understandings of the situations of socio-economically marginalised people in Aotearoa. Significant societal and policy change is required for Māori whānau to be truly free from the cycle of precarity.

text: Precariat Māori households today Aotearoa New Zealand is now the fifth most unequal economy in the OECD. To highlight the human cost of this situation, the concept of “the precariat” offers more informed and contextualised understandings of the situations of socio-economically marginalised people in Aotearoa. Significant societal and policy change is required for Māori whānau to be truly free from the cycle of precarity.

year: 2019