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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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last updated on 2024-07-19 19:19 CEST by the dblp team
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