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pro vyhledávání: '"Grnarova, Paulina"'
Language Modelling has been a central part of Natural Language Processing for a very long time and in the past few years LSTM-based language models have been the go-to method for commercial language modeling. Recently, it has been shown that when loo
Externí odkaz:
http://arxiv.org/abs/2406.10256
Many applications in machine learning can be framed as minimization problems and solved efficiently using gradient-based techniques. However, recent applications of generative models, particularly GANs, have triggered interest in solving min-max game
Externí odkaz:
http://arxiv.org/abs/2103.12685
Autor:
Grnarova, Paulina, Levy, Kfir Y, Lucchi, Aurelien, Perraudin, Nathanael, Goodfellow, Ian, Hofmann, Thomas, Krause, Andreas
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential for: (i) relative assessment of different models and (ii) monitoring th
Externí odkaz:
http://arxiv.org/abs/1811.05512
Recent work has shown that state-of-the-art models are highly vulnerable to adversarial perturbations of the input. We propose cowboy, an approach to detecting and defending against adversarial attacks by using both the discriminator and generator of
Externí odkaz:
http://arxiv.org/abs/1805.10652
We consider the problem of training generative models with a Generative Adversarial Network (GAN). Although GANs can accurately model complex distributions, they are known to be difficult to train due to instabilities caused by a difficult minimax op
Externí odkaz:
http://arxiv.org/abs/1706.03269
Publikováno v:
European Conference on Information Retrieval (ECIR) 2018
Advances in natural language processing tasks have gained momentum in recent years due to the increasingly popular neural network methods. In this paper, we explore deep learning techniques for answering multi-step reasoning questions that operate on
Externí odkaz:
http://arxiv.org/abs/1702.06589
We present an automatic mortality prediction scheme based on the unstructured textual content of clinical notes. Proposing a convolutional document embedding approach, our empirical investigation using the MIMIC-III intensive care database shows sign
Externí odkaz:
http://arxiv.org/abs/1612.00467
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Publikováno v:
New Trends in Networking, Computing, E-learning, Systems Sciences & Engineering; 2015, p243-251, 9p
Akademický článek
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