Select Publications

Robust Authorship Verification with Transfer Learning

Published in CICLing 2019, 2019

We present an end-to-end model-building process that is universally applicable to a wide variety of corpora, and requires little to no modification or fine-tuning.

Recommended citation: Dainis Boumber, Yifan Zhang, Marjan Hosseinia, Arjun Mukherjee, and Ricardo Vilalta. "Robust Authorship Verification with Transfer Learning", Proceedings of the 20th International Computational Linguistics and Intelligent Text Processing Conference, CICLing 2019,, La Rochelle, France, April 7-13, 2019.

Experiments with Convolutional Neural Networks for Multi-Label Authorship Attribution

Published in Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), 2018

We explore the use of Convolutional Neural Networks (CNNs) for multi-label Authorship Attribution (AA) problems and propose a CNN specifically designed for such tasks.

Recommended citation: Dainis Boumber, Yifan Zhang and A. Mukherjee. "Experiments with convolutional neural networks for multi-label authorship attribution." Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018), Paris, France, 2018. European Language Resources Association (ELRA).

A General Approach to Domain Adaptation with Applications in Astronomy

Published in PASP, 2018

We propose a Maximum a Posteriori approach to estimate model complexity in supervised learning by assuming the existence of a previous learning task from which we can build a prior distribution.

Recommended citation: Vilalta R., Dhar Gupta K., Boumber D., Meskhi M. M., “A General Approach to Domain Adaptation with Applications in Astronomy”, Publications of the Astronomical Society of the Pacific (PASP), 2018, IOP Science Press.

Supervised learning to detect salt body

Published in 2015 SEG’s International Exposition and 85th Annual Meeting in New Orleans, Louisiana1, 2015

In this paper we are presenting a novel workflow to detect salt body base on seismic attributes and supervised learning.

Recommended citation: Pablo Guillen (University of Houston), German Larrazabal (Repsol USA), Gladys González (Repsol USA) Dainis Boumber (University of Houston), Ricardo Vilalta (University of Houston), “Supervised learning to detect salt body”, 2015 SEG’s International Exposition and 85th Annual Meeting in New Orleans, Louisiana

Scalable and Fault Resilient Physical Neural Networks on a Single Chip

Published in CASES, 2014

This paper presents a design and implementation of a physical neural network that is resilient to permanent hardware faults.

Recommended citation: W. Shi, Y. Wen, Z. Liu, X. Zhao, D. Boumber, R. Vilalta and L. Xu, “Scalable and Fault Resilient Physical Neural Networks on a Single Chip”, CASES 2014