Zobrazeno 1 - 10
of 102
pro vyhledávání: '"Flierl, Markus"'
Autor:
Rana, Pravin Kumar, Flierl, Markus
Multiview depth imagery will play a critical role in free-viewpoint television. This technology requires high quality virtual view synthesis to enable viewers to move freely in a dynamic real world scene. Depth imagery at different viewpoints is used
Externí odkaz:
http://arxiv.org/abs/2301.11752
We present a representation learning framework for financial time series forecasting. One challenge of using deep learning models for finance forecasting is the shortage of available training data when using small datasets. Direct trend classificatio
Externí odkaz:
http://arxiv.org/abs/2002.07638
Among many current data processing systems, the objectives are often not the reproduction of data, but to compute some answers based on the data resulting from queries. The similarity identification task is to identify the items in a database that ar
Externí odkaz:
http://arxiv.org/abs/2001.07941
Autor:
Wu, Hanwei, Flierl, Markus
Autoencoders and their variations provide unsupervised models for learning low-dimensional representations for downstream tasks. Without proper regularization, autoencoder models are susceptible to the overfitting problem and the so-called posterior
Externí odkaz:
http://arxiv.org/abs/1905.11062
Autor:
Wu, Hanwei, Flierl, Markus
In this paper, we provide an information-theoretic interpretation of the Vector Quantized-Variational Autoencoder (VQ-VAE). We show that the loss function of the original VQ-VAE can be derived from the variational deterministic information bottleneck
Externí odkaz:
http://arxiv.org/abs/1808.01048
Autor:
Wu, Hanwei, Flierl, Markus
Vector-Quantized Variational Autoencoders (VQ-VAE)[1] provide an unsupervised model for learning discrete representations by combining vector quantization and autoencoders. In this paper, we study the use of VQ-VAE for representation learning for dow
Externí odkaz:
http://arxiv.org/abs/1807.04629
Motivated by emerging vision-based intelligent services, we consider the problem of rate adaptation for high quality and low delay visual information delivery over wireless networks using scalable video coding. Rate adaptation in this setting is inhe
Externí odkaz:
http://arxiv.org/abs/1704.02790
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