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Pavlos Protopapas
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2020 – today
- 2024
- [c19]John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin:
IoT Malware Data Augmentation using a Generative Adversarial Network. HICSS 2024: 7572-7581 - [c18]John Carter, Spiros Mancoridis, Pavlos Protopapas, Erick Galinkin:
Behavioral Malware Detection using a Language Model Classifier Trained on sys2vec Embeddings. HICSS 2024: 7582-7591 - [i55]Yago Bea, Raúl Jiménez, David Mateos, Shuheng Liu, Pavlos Protopapas, Pedro Tarancón-Álvarez, Pablo Tejerina-Pérez:
Gravitational Duals from Equations of State. CoRR abs/2403.14763 (2024) - 2023
- [j9]Marios Mattheakis
, Hayden Joy, Pavlos Protopapas:
Reservoir Computing for Solving Ordinary Differential Equations. Int. J. Artif. Intell. Tools 32(1): 2350030:1-2350030:17 (2023) - [c17]Shuheng Liu, Xiyue Huang, Pavlos Protopapas:
Residual-based error bound for physics-informed neural networks. UAI 2023: 1284-1293 - [i54]Shuheng Liu, Xiyue Huang, Pavlos Protopapas:
Residual-based error bound for physics-informed neural networks. CoRR abs/2306.03786 (2023) - [i53]Wanzhou Lei, Pavlos Protopapas, Joy Parikh:
One-Shot Transfer Learning for Nonlinear ODEs. CoRR abs/2311.14931 (2023) - [i52]Arya Mohan, Pavlos Protopapas, Keerthi Kunnumkai, Cecilia Garraffo, Lindy Blackburn, Koushik Chatterjee, Sheperd S. Doeleman, Razieh Emami, Christian M. Fromm, Yosuke Mizuno, Angelo Ricarte:
Generating Images of the M87* Black Hole Using GANs. CoRR abs/2312.01005 (2023) - 2022
- [j8]Gerson R. Santos
, Antonio de Pádua Santos
, Pavlos Protopapas
, Tiago A. E. Ferreira
:
Gravitational wave signal recognition and ring-down time estimation via Artificial Neural Networks. Expert Syst. Appl. 207: 117931 (2022) - [c16]Anwesh Bhattacharya, Marios Mattheakis, Pavlos Protopapas:
Encoding Involutory Invariances in Neural Networks. IJCNN 2022: 1-8 - [c15]Henry Jin, Marios Mattheakis, Pavlos Protopapas:
Physics-Informed Neural Networks for Quantum Eigenvalue Problems. IJCNN 2022: 1-8 - [i51]Henry Jin, Marios Mattheakis, Pavlos Protopapas:
Physics-Informed Neural Networks for Quantum Eigenvalue Problems. CoRR abs/2203.00451 (2022) - [i50]Manuel Pérez-Carrasco, Pavlos Protopapas, Guillermo Cabrera-Vives
:
Con2DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations. CoRR abs/2204.01558 (2022) - [i49]C. Donoso-Oliva, I. Becker, Pavlos Protopapas, Guillermo Cabrera-Vives
, Vishnu M., Harsh Vardhan:
ASTROMER: A transformer-based embedding for the representation of light curves. CoRR abs/2205.01677 (2022) - [i48]Germán García-Jara, Pavlos Protopapas, Pablo A. Estévez
:
Improving Astronomical Time-series Classification via Data Augmentation with Generative Adversarial Networks. CoRR abs/2205.06758 (2022) - [i47]Shuheng Liu, Xiyue Huang, Pavlos Protopapas:
Evaluating Error Bound for Physics-Informed Neural Networks on Linear Dynamical Systems. CoRR abs/2207.01114 (2022) - [i46]Hayden Joy, Marios Mattheakis, Pavlos Protopapas:
RcTorch: a PyTorch Reservoir Computing Package with Automated Hyper-Parameter Optimization. CoRR abs/2207.05870 (2022) - [i45]Blake Bullwinkel, Dylan Randle, Pavlos Protopapas, David Sondak:
DEQGAN: Learning the Loss Function for PINNs with Generative Adversarial Networks. CoRR abs/2209.07081 (2022) - [i44]Raphaël Pellegrin, Blake Bullwinkel, Marios Mattheakis, Pavlos Protopapas:
Transfer Learning with Physics-Informed Neural Networks for Efficient Simulation of Branched Flows. CoRR abs/2211.00214 (2022) - [i43]Félix Grèzes, Thomas Allen, Sergi Blanco-Cuaresma
, Alberto Accomazzi
, Michael J. Kurtz, Golnaz Shapurian, Edwin A. Henneken, Carolyn S. Grant, Donna M. Thompson, Timothy W. Hostetler
, Matthew R. Templeton, Kelly E. Lockhart
, Shinyi Chen, Jennifer Koch, Taylor Jacovich
, Pavlos Protopapas:
Improving astroBERT using Semantic Textual Similarity. CoRR abs/2212.00744 (2022) - [i42]Olga Graf, Pablo Flores, Pavlos Protopapas, Karim Pichara:
Error-Aware B-PINNs: Improving Uncertainty Quantification in Bayesian Physics-Informed Neural Networks. CoRR abs/2212.06965 (2022) - 2021
- [i41]Tiago A. E. Ferreira, Marios Mattheakis, Pavlos Protopapas:
A New Artificial Neuron Proposal with Trainable Simultaneous Local and Global Activation Function. CoRR abs/2101.06100 (2021) - [i40]C. Donoso-Oliva, Guillermo Cabrera-Vives, Pavlos Protopapas, Rodrigo Carrasco-Davis, Pablo A. Estévez:
The effect of phased recurrent units in the classification of multiple catalogs of astronomical lightcurves. CoRR abs/2106.03736 (2021) - [i39]Anwesh Bhattacharya, Marios Mattheakis, Pavlos Protopapas:
Encoding Involutory Invariance in Neural Networks. CoRR abs/2106.12891 (2021) - [i38]Shaan Desai, Marios Mattheakis, David Sondak, Pavlos Protopapas, Stephen J. Roberts:
Port-Hamiltonian Neural Networks for Learning Explicit Time-Dependent Dynamical Systems. CoRR abs/2107.08024 (2021) - [i37]Marios Mattheakis, Hayden Joy, Pavlos Protopapas:
Unsupervised Reservoir Computing for Solving Ordinary Differential Equations. CoRR abs/2108.11417 (2021) - [i36]Shaan Desai, Marios Mattheakis, Hayden Joy, Pavlos Protopapas, Stephen J. Roberts:
One-Shot Transfer Learning of Physics-Informed Neural Networks. CoRR abs/2110.11286 (2021) - [i35]Haitz Sáez de Ocáriz Borde, David Sondak, Pavlos Protopapas:
Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow. CoRR abs/2111.00328 (2021) - [i34]Olga Graf, Pablo Flores, Pavlos Protopapas, Karim Pichara:
Uncertainty Quantification in Neural Differential Equations. CoRR abs/2111.04207 (2021) - [i33]Kshitij Parwani, Pavlos Protopapas:
Adversarial Sampling for Solving Differential Equations with Neural Networks. CoRR abs/2111.12024 (2021) - [i32]Félix Grèzes, Sergi Blanco-Cuaresma
, Alberto Accomazzi
, Michael J. Kurtz
, Golnaz Shapurian, Edwin A. Henneken
, Carolyn S. Grant, Donna M. Thompson, Roman Chyla, Stephen McDonald, Timothy W. Hostetler, Matthew R. Templeton, Kelly E. Lockhart, Nemanja Martinovic
, Shinyi Chen
, Chris Tanner, Pavlos Protopapas:
Building astroBERT, a language model for Astronomy & Astrophysics. CoRR abs/2112.00590 (2021) - 2020
- [j7]Feiyu Chen
, David Sondak, Pavlos Protopapas, Marios Mattheakis, Shuheng Liu, Devansh Agarwal
, Marco Di Giovanni:
NeuroDiffEq: A Python package for solving differential equations with neural networks. J. Open Source Softw. 5(46): 1931 (2020) - [c14]Marco Di Giovanni, David Sondak, Pavlos Protopapas, Marco Brambilla:
Finding Multiple Solutions of ODEs with Neural Networks. AAAI Spring Symposium: MLPS 2020 - [c13]Nicolás Astorga
, Pablo Huijse
, Pavlos Protopapas, Pablo A. Estévez
:
MPCC: Matching Priors and Conditionals for Clustering. ECCV (23) 2020: 658-677 - [c12]Wenying Wu, Panagiotis Michalatos, Pavlos Protopapas, Zheng Yang:
Gender Classification and Bias Mitigation in Facial Images. WebSci 2020: 106-114 - [i31]Marios Mattheakis, David Sondak, Akshunna S. Dogra
, Pavlos Protopapas:
Hamiltonian Neural Networks for solving differential equations. CoRR abs/2001.11107 (2020) - [i30]Ignacio Becker, Karim Pichara, Márcio Catelán
, Pavlos Protopapas, Carlos Aguirre, Fatemeh Nikzat:
Scalable End-to-end Recurrent Neural Network for Variable star classification. CoRR abs/2002.00994 (2020) - [i29]Gerson R. Santos
, Marcela P. Figueiredo, Antonio de Pádua Santos, Pavlos Protopapas, Tiago A. E. Ferreira:
Gravitational Wave Detection and Information Extraction via Neural Networks. CoRR abs/2003.09995 (2020) - [i28]Courtney Cochrane, David Castineira, Nisreen Shiban, Pavlos Protopapas:
Application of Machine Learning to Predict the Risk of Alzheimer's Disease: An Accurate and Practical Solution for Early Diagnostics. CoRR abs/2006.08702 (2020) - [i27]Cedric Flamant, Pavlos Protopapas, David Sondak:
Solving Differential Equations Using Neural Network Solution Bundles. CoRR abs/2006.14372 (2020) - [i26]Wenying Wu, Pavlos Protopapas, Zheng Yang, Panagiotis Michalatos:
Gender Classification and Bias Mitigation in Facial Images. CoRR abs/2007.06141 (2020) - [i25]Dylan Randle, Pavlos Protopapas, David Sondak:
Unsupervised Learning of Solutions to Differential Equations with Generative Adversarial Networks. CoRR abs/2007.11133 (2020) - [i24]Nicolás Astorga, Pablo Huijse, Pavlos Protopapas, Pablo A. Estévez:
MPCC: Matching Priors and Conditionals for Clustering. CoRR abs/2008.09641 (2020) - [i23]Alessandro Paticchio, Tommaso Scarlatti, Marios Mattheakis, Pavlos Protopapas, Marco Brambilla:
Semi-supervised Neural Networks solve an inverse problem for modeling Covid-19 spread. CoRR abs/2010.05074 (2020) - [i22]Henry Jin, Marios Mattheakis, Pavlos Protopapas:
Unsupervised Neural Networks for Quantum Eigenvalue Problems. CoRR abs/2010.05075 (2020)
2010 – 2019
- 2019
- [c11]M. Manjunathaiah, Andrew Meade, R. Thavarajan, Pavlos Protopapas, R. Dave:
Big Data Scalability of BayesPhylogenies on Harvard's Ozone 12k Cores. BIOINFORMATICS 2019: 143-148 - [c10]Belén Saldías-Fuentes, Pavlos Protopapas, Karim Pichara B.:
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification. SDM 2019: 756-764 - [i21]Belén Saldías-Fuentes, Pavlos Protopapas, Karim Pichara B.:
A Full Probabilistic Model for Yes/No Type Crowdsourcing in Multi-Class Classification. CoRR abs/1901.00397 (2019) - [i20]Christian Pieringer, Karim Pichara, Márcio Catelán
, Pavlos Protopapas:
An Algorithm for the Visualization of Relevant Patterns in Astronomical Light Curves. CoRR abs/1903.03254 (2019) - [i19]Jacob Reinier Maat, Nikos Gianniotis, Pavlos Protopapas:
Efficient Optimization of Echo State Networks for Time Series Datasets. CoRR abs/1903.05071 (2019) - [i18]Alessandro Bianchi, Moreno Raimondo Vendra, Pavlos Protopapas, Marco Brambilla:
Improving Image Classification Robustness through Selective CNN-Filters Fine-Tuning. CoRR abs/1904.03949 (2019) - [i17]Marios Mattheakis, Pavlos Protopapas, David Sondak, Marco Di Giovanni, Efthimios Kaxiras:
Physical Symmetries Embedded in Neural Networks. CoRR abs/1904.08991 (2019) - [i16]Manuel Pérez-Carrasco, Guillermo Cabrera-Vives, Pavlos Protopapas, Nicolas Astorga, Marouan Belhaj:
Adversarial Variational Domain Adaptation. CoRR abs/1909.11651 (2019) - [i15]Javiera Astudillo, Pavlos Protopapas, Karim Pichara, Pablo Huijse:
An Information Theory Approach on Deciding Spectroscopic Follow Ups. CoRR abs/1911.02444 (2019) - [i14]Lukas Zorich, Karim Pichara, Pavlos Protopapas:
Streaming Classification of Variable Stars. CoRR abs/1912.02235 (2019) - 2018
- [c9]Nicholas Hoernle, Ya'akov Gal, Barbara J. Grosz, Pavlos Protopapas, Andee Rubin:
Modeling the Effects of Students' Interactions with Immersive Simulations using Markov Switching Systems. EDM 2018 - [c8]Jacob Reinier Maat
, Nikos Gianniotis
, Pavlos Protopapas:
Efficient Optimization of Echo State Networks for Time Series Datasets. IJCNN 2018: 1-7 - [i13]Giorgia Ramponi, Pavlos Protopapas, Marco Brambilla, Ryan Janssen:
T-CGAN: Conditional Generative Adversarial Network for Data Augmentation in Noisy Time Series with Irregular Sampling. CoRR abs/1811.08295 (2018) - [i12]Marouan Belhaj, Pavlos Protopapas, Weiwei Pan:
Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models. CoRR abs/1812.03123 (2018) - 2017
- [i11]Pablo Huijse, Pablo A. Estévez, Francisco Förster
, Scott F. Daniel, Andrew J. Connolly, Pavlos Protopapas, Rodrigo Carrasco, José C. Príncipe:
Robust period estimation using mutual information for multi-band light curves in the synoptic survey era. CoRR abs/1709.03541 (2017) - 2016
- [c7]Harikrishna Narasimhan, Weiwei Pan, Purushottam Kar, Pavlos Protopapas, Harish G. Ramaswamy:
Optimizing the Multiclass F-Measure via Biconcave Programming. ICDM 2016: 1101-1106 - [c6]Xide Xia, Pavlos Protopapas, Finale Doshi-Velez:
Cost-Sensitive Batch Mode Active Learning: Designing Astronomical Observation by Optimizing Telescope Time and Telescope Choice. SDM 2016: 477-485 - [i10]Cristóbal Mackenzie, Karim Pichara, Pavlos Protopapas:
Clustering Based Feature Learning on Variable Stars. CoRR abs/1602.08977 (2016) - 2015
- [i9]Pablo Huijse, Pablo A. Estévez, Pavlos Protopapas, José C. Príncipe, Pablo Zegers:
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases. CoRR abs/1509.07823 (2015) - 2014
- [j6]Pablo Huijse
, Pablo A. Estévez, Pavlos Protopapas, José C. Príncipe, Pablo Zegers:
Computational Intelligence Challenges and Applications on Large-Scale Astronomical Time Series Databases. IEEE Comput. Intell. Mag. 9(3): 27-39 (2014) - [i8]Isadora nun, Karim Pichara, Pavlos Protopapas, Dae-Won Kim:
Supervised detection of anomalous light-curves in massive astronomical catalogs. CoRR abs/1404.4888 (2014) - [i7]Pavlos Protopapas, Pablo Huijse, Pablo A. Estévez, Pablo Zegers, José C. Príncipe:
A Novel, Fully Automated Pipeline for Period Estimation in the EROS 2 Data Set. CoRR abs/1412.1840 (2014) - 2013
- [i6]Karim Pichara, Pavlos Protopapas:
Automatic Classification of Variable Stars in Catalogs with missing data. CoRR abs/1310.7868 (2013) - 2012
- [j5]Pablo Huijse
, Pablo A. Estévez, Pavlos Protopapas, Pablo Zegers, José Carlos Príncipe:
An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves. IEEE Trans. Signal Process. 60(10): 5135-5145 (2012) - [i5]Yuyang Wang, Roni Khardon, Pavlos Protopapas:
Infinite Shift-invariant Grouped Multi-task Learning for Gaussian Processes. CoRR abs/1203.0970 (2012) - [i4]Pablo Huijse, Pablo A. Estévez, Pavlos Protopapas, Pablo Zegers, José C. Príncipe:
An Information Theoretic Algorithm for Finding Periodicities in Stellar Light Curves. CoRR abs/1212.2398 (2012) - 2011
- [j4]Pablo Huijse
, Pablo A. Estévez, Pablo Zegers, José Carlos Príncipe, Pavlos Protopapas:
Period Estimation in Astronomical Time Series Using Slotted Correntropy. IEEE Signal Process. Lett. 18(6): 371-374 (2011) - [c5]Bibhu Prasad Mishra, José C. Príncipe, Pablo A. Estévez
, Pavlos Protopapas:
Estimation of periodicity in non-uniformly sampled astronomical data using a 2D kernel in correntropy. MLSP 2011: 1-6 - [i3]Yuyang Wang, Roni Khardon, Pavlos Protopapas:
Nonparametric Bayesian Estimation of Periodic Functions. CoRR abs/1111.1315 (2011) - [i2]Pablo Huijse, Pablo A. Estévez, Pablo Zegers, José Carlos Príncipe, Pavlos Protopapas:
Period Estimation in Astronomical Time Series Using Slotted Correntropy. CoRR abs/1112.2962 (2011) - 2010
- [c4]Pablo A. Estévez, Pablo Huijse
, Pablo Zegers, José C. Príncipe, Pavlos Protopapas:
Period detection in light curves from astronomical objects using correntropy. IJCNN 2010: 1-7 - [c3]Yuyang Wang, Roni Khardon, Pavlos Protopapas:
Shift-Invariant Grouped Multi-task Learning for Gaussian Processes. ECML/PKDD (3) 2010: 418-434
2000 – 2009
- 2009
- [j3]Umaa Rebbapragada, Pavlos Protopapas, Carla E. Brodley, Charles R. Alcock:
Finding anomalous periodic time series. Mach. Learn. 74(3): 281-313 (2009) - [j2]Dan Preston, Pavlos Protopapas, Carla E. Brodley:
Discovering arbitrary event types in time series. Stat. Anal. Data Min. 2(5-6): 396-411 (2009) - [j1]Eamonn J. Keogh, Li Wei, Xiaopeng Xi, Michail Vlachos
, Sang-Hee Lee, Pavlos Protopapas:
Supporting exact indexing of arbitrarily rotated shapes and periodic time series under Euclidean and warping distance measures. VLDB J. 18(3): 611-630 (2009) - [c2]Gabriel Wachman, Roni Khardon, Pavlos Protopapas, Charles R. Alcock:
Kernels for Periodic Time Series Arising in Astronomy. ECML/PKDD (2) 2009: 489-505 - [c1]Dan Preston, Pavlos Protopapas, Carla E. Brodley:
Event Discovery in Time Series. SDM 2009: 61-72 - [i1]Umaa Rebbapragada, Pavlos Protopapas, Carla E. Brodley, Charles R. Alcock:
Finding Anomalous Periodic Time Series: An Application to Catalogs of Periodic Variable Stars. CoRR abs/0905.3428 (2009)
Coauthor Index
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