Pablo M. Olmos

me 

Research interests

My research interests are in the area of digital communications and machine learning. Currently, I work on the following problems:

  • Analysis and design of finite-length SC-LDPC codes and Generalized LDPC codes.

  • Expectation Consistency methods for Approximate Inference in Probabilistic Graphical Models.

  • Bayesian Multi-view Learning for Heterogeneous Database Integration.

  • Topic Modeling and Text Recognition in Historical Documents.

Latest news!

Files for the Tensor Flow Course uploaded (March 2017)

Python notebooks to get started with Tensorflow, Neural Neworks (NNs), Convolutional NNs, Word Embeddings and Recurrent Neural Networks. This is a personal wrap-up of all the material provided by Google's Deep Learning course on Udacity, so all credit goes to them.

You can find the material in the code section.

Paper accepted at IEEE Transactions on Wireless Communications (March 2017)

Probabilistic Equalization with a Smoothing Expectation Propagation Approach. Irene Santos, Juan José Murillo-Fuentes, Eva Arias-de-Reyna, and Pablo M. Olmos.

Paper accepted at MDPI Entropy (December 2016)

Entropy-Constrained Scalar Quantization with a Lossy-Compressed Bit. Melanie F. Pradier, Pablo M. Olmos and Fernando Perez-Cruz.