Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Yung, Jessica"'
This paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that
Externí odkaz:
http://arxiv.org/abs/2109.09358
Autor:
Tolstikhin, Ilya, Houlsby, Neil, Kolesnikov, Alexander, Beyer, Lucas, Zhai, Xiaohua, Unterthiner, Thomas, Yung, Jessica, Steiner, Andreas, Keysers, Daniel, Uszkoreit, Jakob, Lucic, Mario, Dosovitskiy, Alexey
Convolutional Neural Networks (CNNs) are the go-to model for computer vision. Recently, attention-based networks, such as the Vision Transformer, have also become popular. In this paper we show that while convolutions and attention are both sufficien
Externí odkaz:
http://arxiv.org/abs/2105.01601
Autor:
Yung, Jessica, Romijnders, Rob, Kolesnikov, Alexander, Beyer, Lucas, Djolonga, Josip, Houlsby, Neil, Gelly, Sylvain, Lucic, Mario, Zhai, Xiaohua
Before deploying machine learning models it is critical to assess their robustness. In the context of deep neural networks for image understanding, changing the object location, rotation and size may affect the predictions in non-trivial ways. In thi
Externí odkaz:
http://arxiv.org/abs/2104.04191
Autor:
Djolonga, Josip, Yung, Jessica, Tschannen, Michael, Romijnders, Rob, Beyer, Lucas, Kolesnikov, Alexander, Puigcerver, Joan, Minderer, Matthias, D'Amour, Alexander, Moldovan, Dan, Gelly, Sylvain, Houlsby, Neil, Zhai, Xiaohua, Lucic, Mario
Modern deep convolutional networks (CNNs) are often criticized for not generalizing under distributional shifts. However, several recent breakthroughs in transfer learning suggest that these networks can cope with severe distribution shifts and succe
Externí odkaz:
http://arxiv.org/abs/2007.08558
Autor:
Kolesnikov, Alexander, Beyer, Lucas, Zhai, Xiaohua, Puigcerver, Joan, Yung, Jessica, Gelly, Sylvain, Houlsby, Neil
Transfer of pre-trained representations improves sample efficiency and simplifies hyperparameter tuning when training deep neural networks for vision. We revisit the paradigm of pre-training on large supervised datasets and fine-tuning the model on a
Externí odkaz:
http://arxiv.org/abs/1912.11370
Autor:
L��pez, Federico, Scholz, Martin, Yung, Jessica, Pellat, Marie, Strube, Michael, Dixon, Lucas
This paper introduces a simple and effective form of data augmentation for recommender systems. A paraphrase similarity model is applied to widely available textual data, such as reviews and product descriptions, yielding new semantic relations that
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::e5b65b82a041c81642843948aef15033
http://arxiv.org/abs/2109.09358
http://arxiv.org/abs/2109.09358
Autor:
Ferrari, Rossella
Publikováno v:
TDR: The Drama Review (MIT Press); Fall2017, Vol. 61 Issue 3, p141-164, 24p, 12 Color Photographs, 1 Black and White Photograph
Autor:
Rossella Ferrari, Ashley Thorpe
Asian City Crossings is the first volume to examine the relationship between the city and performance from an Asian perspective. This collection introduces'city as method'as a new conceptual framework for the investigation of practices of city-based