I am currently a research engineer at École Polytechnique Fédérale de Lausanne in Switzerland where I develop machine learning solutions tailored for analysing social media. I recently finished my Ph.D working on deep learning models for natural language processing with Ronan Collobert at the Idiap Research Institute. Before my Ph.D, I worked as an engineer where I developed different data analysis tools for large-scale applications, such as in genetics.
My main research interests are in natural language processing, AI, and machine learning. I’m particularly focused on building models for large-scale datasets, which led me into deep learning models. As a former engineer I always look for fast systems that work well in practice.
- hpca, this toolkit provides an efficient implementation of the Hellinger PCA for computing word embeddings.
Get the source code from GitHub.
- Rmixmod, an interface for MIXMOD which provides a tool for supervised and unsupervised classification on qualitative and quantitative data.
This package is available on CRAN.
(Check out my Google Scholar)
- R. Lebret, D. Grangier, M. Auli. Neural Text Generation from Structured Data with Application to the Biography Domain. In EMNLP, 2016 [PDF] [Dataset]
- R. Lebret, R. Collobert. “The Sum of Its Parts”: Joint Learning of Word and Phrase Representations with Autoencoders. In ICML Deep Learning Workshop, 2015. [PDF]
- R. Lebret, P. O. Pinheiro, R. Collobert. Simple Image Description Generator via a Linear Phrase-Based Model. In ICLR Workshop, 2015. [PDF]
- R. Lebret, R. Collobert. N-gram-Based Low-Dimensional Representation for Document Classification. In ICLR Workshop, 2015. [PDF]
- R. Lebret, R. Collobert. Rehabilitation of Count-based Models for Word Vector Representations. In CICLing, 2015. [PDF][Bibtex]
- R. Lebret, R. Collobert. Word Embeddings through Hellinger PCA. In EACL, 2014. [PDF] [Talk][BibTex] [Code]
- R. Lebret, J. Legrand, R. Collobert. Is Deep Learning Really Necessary for Word Embeddings? In NIPS Workshop on Deep Learning, 2013. [PDF]
- R. Lebret, S. Iovleff, F. Langrognet, C. Biernacki, G. Celeux, G. Govaert. Rmixmod: The R Package of the Model-Based Unsupervised, Supervised and Semi-Supervised Classification Mixmod Library. In Journal of Statistical Software, 67(1):1–29, 2015. [PDF]