| Approximating likelihood ratios with calibrated discriminative classifiers K Cranmer, J Pavez, G Louppe arXiv preprint arXiv:1506.02169, 2015 | 223 | 2015 |
| Constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical review letters 121 (11), 111801, 2018 | 193 | 2018 |
| Mining gold from implicit models to improve likelihood-free inference J Brehmer, G Louppe, J Pavez, K Cranmer Proceedings of the National Academy of Sciences 117 (10), 5242-5249, 2020 | 180 | 2020 |
| A guide to constraining effective field theories with machine learning J Brehmer, K Cranmer, G Louppe, J Pavez Physical Review D 98 (5), 052004, 2018 | 175 | 2018 |
| Likelihood-free inference with an improved cross-entropy estimator M Stoye, J Brehmer, G Louppe, J Pavez, K Cranmer arXiv preprint arXiv:1808.00973, 2018 | 50 | 2018 |
| Working memory networks: Augmenting memory networks with a relational reasoning module J Pavez, H Allende, H Allende-Cid arXiv preprint arXiv:1805.09354, 2018 | 28 | 2018 |
| Effective LHC measurements with matrix elements and machine learning J Brehmer, K Cranmer, I Espejo, F Kling, G Louppe, J Pavez Journal of Physics: Conference Series 1525 (1), 012022, 2020 | 23 | 2020 |
| Large scale cathodic exfoliation of graphite using deep eutectic solvent and water mixture D Vásquez-Sandoval, J Pavez, C Carlesi, A Aracena Fullerenes, Nanotubes and Carbon Nanostructures 26 (2), 123-129, 2018 | 14 | 2018 |
| Experiments using machine learning to approximate likelihood ratios for mixture models K Cranmer, J Pavez, G Louppe, WK Brooks Journal of Physics: Conference Series 762 (1), 012034, 2016 | 11 | 2016 |
| carl: a likelihood-free inference toolbox. G Louppe, K Cranmer, J Pavez J. Open Source Softw. 1 (1), 11, 2016 | 10 | 2016 |
| WriteWise: software that guides scientific writing EN Fuentes, H Allende-Cid, S Rodríguez, R Venegas, J Pavez, W Palma, ... DE LINGÜÍSTICA COMPUTACIONAL Y DE CORPUS, 332, 2020 | 2 | 2020 |
| Sentence encoders as a method for helping users identify and improve semantic similarity in bio-medical text B Díaz, J Pávez, S Rodríguez, W Palma, H Allende-Cid, R Venegas, ... DE LINGÜÍSTICA COMPUTACIONAL Y DE CORPUS, 327, 2020 | | 2020 |
| A deep-learning model for discursive segmentation shows high accuracies for sentences classification in biomedical scientific papers J Pavez, S Rodríguez, EN Fuentes DE LINGÜÍSTICA COMPUTACIONAL Y DE CORPUS, 32, 2020 | | 2020 |
| WriteWise: software that guides scientific writing EF Caro, H Allende-Cid, S Rodríguez, R Venegas, J Pavez, W Palma, ... Actas III Congreso Internacional de Lingüística Computacional y de Corpus …, 2020 | | 2020 |
| Constraining Effective Field Theories with Machine Learning J Pavez, K Cranmer, J Brehmer, G Louppe Physical Review Letters 121 (arXiv: 1805.00013), 2018 | | 2018 |
| Neural networks for the reconstruction and separation of high energy particles in a preshower calorimeter J Pavez, H Hakobyan, C Valle, W Brooks, S Kuleshov, H Allende Progress in Pattern Recognition, Image Analysis, Computer Vision, and …, 2018 | | 2018 |
| Approximating likelihood ratios with calibrated classifiers G Louppe, K Cranmer, J Pavez Second Machine Learning in High Energy Physics Summer School 2016, 2016 | | 2016 |
| Revealing the collaborative dynamics of a large-scale arXiv text collection by means of k-shell decomposition J VERA, W PALMA, H ALLENDE, S RODRIGUEZ, J PAVEZ, E FUENTES | | |
| Working Memory Networks J Pavez, H Allende, H Allende-Cid | | |
