Department of Computer Science | Institute of Theoretical Computer Science | CADMO

Theory of Combinatorial Algorithms

Prof. Emo Welzl and Prof. Bernd Gärtner

Mittagsseminar (in cooperation with M. Ghaffari, A. Steger and B. Sudakov)

Mittagsseminar Talk Information

Date and Time: Thursday, November 16, 2017, 12:15 pm

Duration: 30 minutes

Location: CAB G51

Speaker: Asier Mujika

Fast-Slow Recurrent Neural Networks

Processing sequential data of variable length is a major challenge in a wide range of applications, such as speech recognition, language modeling, generative image modeling and machine translation. We address this challenge by proposing a novel recurrent neural network (RNN) architecture, the Fast-Slow RNN (FS-RNN). The FS-RNN incorporates the strengths of both multiscale RNNs and deep transition RNNs as it processes sequential data on different timescales and learns complex transition functions from one time step to the next. We evaluate the FS-RNN on character level language modeling data sets. Our approach outperforms the best known compression algorithms on Wikipedia data. We also present an empirical investigation of the learning and network dynamics of the FS-RNN, which explains the improved performance compared to other RNN architectures. The first part of this talk will cover the basics of recurrent networks, such that anyone should be able to follow the rest. Joint work with Florian Meier and Angelika Steger.


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