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8 papers · 1 filter
Attention-Based Models for Speech Recognition
Jan Chorowski, Dzmitry Bahdanau, Dmitriy Serdyuk +2
Recurrent sequence generators conditioned on input data through an attention mechanism have recently shown very good performance on a range of tasks in- cluding machine translation…
Blocks and Fuel: Frameworks for deep learning
Bart van Merriënboer, Dzmitry Bahdanau, Vincent Dumoulin +4
We introduce two Python frameworks to train neural networks on large datasets: Blocks and Fuel. Blocks is based on Theano, a linear algebra compiler with CUDA-support. It facilitat…
Network heterogeneity and node capacity lead to heterogeneous scaling of fluctuations in random walks on graphs
Kosmas Kosmidis, Moritz Beber, Marc-Thorsten Hütt
Random walks are one of the best investigated dynamical processes on graphs. A particularly fascinating phenomenon is the scaling relationship of fluctuations with the average…
Sampling of stochastic operators
Götz E. Pfander, Pavel Zheltov
We develop sampling methodology aimed at determining stochastic operators that satisfy a support size restriction on the autocorrelation of the operators stochastic spreading funct…
Identification of stochastic operators
Götz E. Pfander, Pavel Zheltov
Based on the here developed functional analytic machinery we extend the theory of operator sampling and identification to apply to operators with stochastic spreading functions. We…
Boundedness of Pseudo-Differential Operators on , Sobolev, and Modulation Spaces
Shahla Molahajloo, Götz E. Pfander
We introduce new classes of modulation spaces over phase space. By means of the Kohn-Nirenberg correspondence, these spaces induce norms on pseudo-differential operators that bound…