Published in CIKM, 2021
In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification.
Recommended citation: Austin Derrow-Pinion, Jennifer She, David Wong, Oliver Lange, Todd Hester, Luis Perez, Marc Nunkesser, Seongjae Lee, Xueying Guo, Brett Wiltshire, et al. Eta prediction with graph neural networks in google maps. CIKM, 2021. https://dl.acm.org/doi/abs/10.1145/3459637.3481916
Published in arxiv, 2021
In this paper, we explore and compare multiple algorithms for solving the complex strategy game of Terra Mystica, hereafter abbreviated as TM. Previous work in the area of super-human game-play using AI has proven effective, with recent break-through for generic algorithms in games such as Go, Chess, and Shogi.
Recommended citation: L Perez - arXiv preprint arXiv:2102.10540, 2021 https://arxiv.org/abs/2102.10540
Published in arxiv, 2021
In this paper, we propose an end-to-end machine learning model for code generation in the Python language built on-top of pretrained language models.
Recommended citation: L Perez, L Ottens, S Viswanathan - arXiv preprint arXiv:2102.10535, 2021 https://arxiv.org/abs/2102.10535
Published in VLDB, 2019
Procella serves hundreds of billions of queries per day across all four workloads at YouTube and several other Google product areas.
Recommended citation: B. Chattopadhyay, P. Dutta, L. Perez, O. Tinn, A. Mccormick, A. Mokashi, P. Harvey, H. Gonzalez, D. Lomax, S. Mittal, et al. Procella: Unifying Serving and Analytical Data at YouTube. PVLDB, 12(12), 2019 https://research.google/pubs/pub48388/
Published in pre-print, 2018
In this paper, we explore several extensions of BERT for the Stanford Question and Answer v2. 0 task in NLP, and summarize our analyzes of these results.
Recommended citation: Perez, Luis. “The Effect of Embeddings on SQuAD v 2 . 0.” (2019). https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1194/reports/default/15709657.pdf
Published in MDPI, 2018
RDataTracker is an R package that collects data provenance from R scripts (https://github.com/End-to-end-provenance/RDataTracker)
Recommended citation: Lerner, B.; Boose, E.; Perez, L. Using Introspection to Collect Provenance in R. Informatics 2018, 5, 12. https://doi.org/10.3390/informatics5010012 https://www.mdpi.com/2227-9709/5/1/12
Published in arxiv cs.CV, 2017
In this paper, we explore and compare multiple solutions to the problem of data augmentation in image classification.
Recommended citation: L Perez, J Wang - arXiv preprint arXiv:1712.04621, 2017 - arxiv.org https://arxiv.org/abs/1712.04621
Published in arxiv, 2017
In this paper, we tackle the real-world problem of predicting Yelp star-review rating based on business features (such as images, descriptions), user features (average previous ratings), and, of particular interest, network properties (which businesses has a user rated before).
Recommended citation: L Perez - arXiv preprint arXiv:1712.04350, 2017 https://arxiv.org/abs/1712.04350
Published in pre-print, 2014
RDataTracker is a library of R functions that collects data provenance in the form of a Data Derivation Graph (DDG) when executing an R script or working in the R console.
Recommended citation: ER Boose, H Forest, BS Lerner, LA Perez - 2014 /publication/2014-08-29-using-rdatatracker