| Centralized student performance prediction in large courses based on low-cost variables in an institutional context A Sandoval, C Gonzalez, R Alarcon, K Pichara, M Montenegro The Internet and Higher Education 37, 76-89, 2018 | 79 | 2018 |
| Supervised detection of anomalous light-curves in massive astronomical catalogs I Nun, K Pichara, P Protopapas, DW Kim The Astrophysical Journal 793 (1), 2014 | 69 | 2014 |
| FATS: FEATURE ANALYSIS FOR TIME SERIES I Nun, P Protopapas, B Sim, M Zhu, R Dave, N Castro, K Pichara http://arxiv.org/abs/1506.00010, 2015 | 61 | 2015 |
| Clustering-based feature learning on variable stars C Mackenzie, K Pichara, P Protopapas The Astrophysical Journal 820 (2), 138, 2016 | 53 | 2016 |
| Automatic classification of variable stars in catalogs with missing data K Pichara, P Protopapas The Astrophysical Journal 777 (2), 83, 2013 | 52 | 2013 |
| Scalable end-to-end recurrent neural network for variable star classification I Becker, K Pichara, M Catelan, P Protopapas, C Aguirre, F Nikzat Monthly Notices of the Royal Astronomical Society 493 (2), 2981-2995, 2020 | 50 | 2020 |
| An improved quasar detection method in EROS‐2 and MACHO LMC data sets K Pichara, P Protopapas, DW Kim, JB Marquette, P Tisserand Monthly Notices of the Royal Astronomical Society 427 (2), 1284-1297, 2012 | 50 | 2012 |
| The vvv templates project towards an automated classification of vvv light-curves-i. building a database of stellar variability in the near-infrared R Angeloni, RC Ramos, M Catelan, I Dekany, F Gran, J Alonso-García, ... Astronomy & Astrophysics 567, A100, 2014 | 47 | 2014 |
| Deep multi-survey classification of variable stars C Aguirre, K Pichara, I Becker Monthly Notices of the Royal Astronomical Society 482 (4), 5078-5092, 2019 | 43 | 2019 |
| Unsupervised classification of variable stars L Valenzuela, K Pichara Monthly Notices of the Royal Astronomical Society 474 (3), 3259-3272, 2018 | 40 | 2018 |
| Meta Classification for Variable Stars K Pichara, P Protopapas, D León The Astrophysical Journal, 2016 | 39 | 2016 |
| Photometric classification of quasars from RCS-2 using Random Forest BCHSL D. Carrasco, L. F. Barrientos, K. Pichara, T. Anguita, D. N. A. Murphy ... Astronomy & Astrophysics 584 (A44), 17, 2015 | 37* | 2015 |
| Photometric Classification of quasars from RCS-2 using Random Forest D Carrasco, F Barrientos, K Pichara, T Anguita, D Murphy, D Gilbank, ... sumitted to the Astrophysical Journal, 2014 | 37 | 2014 |
| Automatic survey-invariant classification of variable stars P Benavente, P Protopapas, K Pichara The Astrophysical Journal 845 (2), 147, 2017 | 32 | 2017 |
| Active learning and subspace clustering for anomaly detection K Pichara, A Soto Intelligent Data Analysis 15 (2), 151-171, 2011 | 25 | 2011 |
| The VISTA Variables in the Vía Láctea infrared variability catalogue (VIVA-I) CE Ferreira Lopes, NJG Cross, M Catelan, D Minniti, M Hempel, ... Monthly Notices of the Royal Astronomical Society 496 (2), 1730-1756, 2020 | 22 | 2020 |
| Uncertain classification of variable stars: handling observational GAPS and noise N Castro, P Protopapas, K Pichara The Astronomical Journal 155 (1), 16, 2017 | 22 | 2017 |
| Systems and methods to mimic target food items using artificial intelligence K Pichara, P Zamora, M Muchnick, O Vásquez US Patent 11,164,478, 2021 | 15 | 2021 |
| Methods to predict food color and recommend changes to achieve a target food color K Pichara, P Zamora, M Muchnick, Y Lerner, O Lerner US Patent 10,970,621, 2021 | 15 | 2021 |
| Understanding learning resources metadata for primary and secondary education M Peralta, R Alarcon, K Pichara, T Mery, F Cano, J Bozo IEEE Transactions on Learning Technologies 11 (4), 456-467, 2017 | 14 | 2017 |
Márcio CatelanPontificia Universidad Católica de Chile在 astro.puc.cl 的电子邮件经过验证
