I am a student at the Institute of Computer Science at Free University of Berlin. My research interests include (modular/hierarchical) reinforcement learning, (stochastic/black-box) optimization and (deep/recurrent) neural networks. I have used these techniques in application domains ranging from computer vision and neural decoding to localization and robotics.
Here is a list of publications, listed in reverse order of publication. I also have a Google Scholar profile although the information there may not always be up-to-date.
- M. Edel, E. Köppe. "Deep Neural Networks for Multimodal Wearable Activity Recognition", in preparation.
- S. Bhardwaj, R. R. Curtin, M. Edel, Y. Mentekidis, C. Sanderson. "ensmallen: a flexible C++ library for efficient function optimization", in NeurIPS 2018 Workshop on Systems for ML, 2018. [bib] [pdf] [code]
- M. Edel. "mlpack open-source machine learning library and community", in NeurIPS 2018 Workshop on Machine Learning Open Source Software, 2018. [bib] [pdf] [code]
- R.R. Curtin, M. Edel, M. Lozhnikov, Y. Mentekidis, S. Ghaisas, S. Zhang. "mlpack: a fast, flexible machine learning library", The Journal of Open Source Software, vol. 3, issue 26, pp. [bib] [pdf] [code]
- R. R. Curtin, S. Bhardwaj, M. Edel, Y. Mentekidis. "A generic and fast C++ optimization framework", arXiv preprint arXiv:1711.06581, 2017. [bib] [pdf]
- R.R. Curtin, M. Edel. "Designing and building the mlpack open-source machine learning library", submitted to The Fourth International Conference of PUST (ICOPUST), 2017. [pdf] [code]
- M. Edel, J. Lausch. "Capacity Visual Attention Networks", in Second Global Conference on
Artificial Intelligence (GCAI), Proceeedings (Christoph Benzmüller Raul Rojas, Geoff Sutcliffe, eds.), EasyChair, EPiC Series in Computing, 2016. [bib] [pdf]
- M. Edel, E. Köppe. "Binarized-BLSTM-RNN based Human Activity Recognition", in Proceedings of the 2016 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2016. [bib] [pdf]
- M. Edel, E. Köppe. "An Advanced Method for Pedestrian Dead Reckoning using BLSTM-RNNs", in Proceedings of the 2015 IEEE International Conference on Indoor Positioning and Indoor Navigation (IPIN), 2015. [bib] [pdf]
- M. Edel, A. Soni, R.R. Curtin. "An automatic benchmarking system", in NIPS 2014 Workshop on Software Engineering for Machine Learning, 2014. [bib] [pdf] [code]
Code & Demos
In order to make my research as reproducible as possible, most of the algorithms and benchmarks I use are available open-source through the mlpack machine learning library. Here is a list of demos, that entirely run in your browser. No software requirements, no compilers, no installations, no GPUs.