Christopher Brix

Computer Science Master Student at RWTH Aachen, Germany

Hello! I'm a computer science student with a focus on machine learning. As a student research assistant, I've gained experience in machine translation research using neural networks and co-authored 2 publications. Currently, I'm focusing on the application of two-dimensional LSTM cells in neural machine translation.

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Latest publications:

Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation, published in EMNLP 2018

Empirical Investigation of Optimization Algorithms in Neural Machine Translation, published in the PBML

Education.

  • 2018-today

    M.Sc. Computer Science, RWTH Aachen, Germany

    I've continued to work as a student research assistant, co-authoring two publications.

  • 2014-2018

    B.Sc. Computer Science, RWTH Aachen, Germany

    Early on, I decided to specialize in machine learning, and attended several lectures and seminars on this topic. I also started to work as a student research assistant at our local chair for natural language processing. My bachelor thesis "Extension of the Attention Mechanism in Neural Machine Translation" dealt with the application of two-dimensional LSTM cells that could replace the attention mechanism in neural machine translation. It was graded 1.2. Final overall grade: 1.6

  • 2006-2014

    Konrad-Heresbach-Gymnasium Mettmann, Germany

    In high school, I've focussed on math, chemistry and computer science. As an additional project, I've teamed up with 2 friends and programmed an online browser-based economics simulation. In my free time, I became a tutor and mediator for younger students. Final grade: 1.0

Source: Peter Winandy

Publications.

Towards Two-Dimensional Sequence to Sequence Model in Neural Machine Translation

P. Bahar, C. Brix, H. Ney. Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3009-3015. Association for Computational Linguistics

We describe a novel network architecture: A two-dimensional LSTM can process two separate sequences at the same time. By feeding in the source sentence, as well as the partial target hypothesis, the 2D-LSTM can internally attend to the relevant source positions. Therefore, we are able to use the final 2D-LSTM output directly to predict the next target token, and do not need an explicit decoder.

Read the paper

Extension of the Attention Mechanism in Neural Machine Translation

C. Brix (2018). Bachelor thesis. RWTH Aachen, Germany

For my bachelor thesis, I've analyzed several alternatives to the attention mechanism which usually determines which source positions to attend to. Besides a technique to re-encode the source sentence dependend on the partial target hypothesis, I've experimented with the usage of one- and two-dimensional LSTMs that summarize the source sentence. I also report first results for a network which solely consist of a two-dimensional LSTM and no additional decoder structure. This work later resulted in the EMNLP paper. If you are interested in the gradient calculations for multi-dimensional LSTMs, have a look!

Read the thesis

Empirical Investigation of Optimization Algorithms in Neural Machine Translation

P. Bahar, T. Alkhouli, J.-T. Peter, C. Brix, and H. Ney. The Prague Bulletin of Mathematical Linguistics, 108(1):13–25, 2017

In order to optimize neural networks, there are a lot of different optimization algorithms. In this paper, we compare and evaluate them with respect to convergence speed, translation quality, and training stability. We also report results on combinations of multiple algorithms.

Read the paper

Highlights.

Photographer: Martin Breuer
  • 2019

    LxMLS, Monitor

    This year, I will help to organize the summer school. During the practical lab sessions, I will supervise and teach the other participating students.

  • 2019

    3rd Google NLP Summit

    As one of a limited number of master students, I've been accepted for the 3rd NLP Summit organized by Google. In Zurich, we will explore cutting edge NLP technology together with the researchers and engineers working on these topics in the industry.

  • 2018

    LxMLS, Participant

    I participated in the Lisbon Machine Learning Summer School. We learned about classification, structured prediction of sequences, trees and graphs, parsing, and deep learning. Theoretial lectures were combined with practical lab sessions and talks from high-profile professionals. Acceptance rate < 40%.

  • 2018

    Supported by the scholarship "Deutschlandstipendium"

  • 2017

    Member of the Dean's List

  • 2016

    Supported by the scholarship "Deutschlandstipendium"

  • 2016

    Tutor for the lecture "Datastructures and Algorithms"

    In weekly meetings, I explained the content of the lecture to a group of about 20 students. I was responsible for grading their homework and answering any questions. Topics included runtime analysis, formal proof of correctness, and sorting algorithms.

  • 2016

    Member of the Dean's List

Contact.

Christopher.Brix@rwth-aachen.de
  • Christopher Brix
  • Martin-Luther-Str. 14
  • 52062 Aachen
  • Germany
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