Poorya ZareMoodi
Poorya ZareMoodi
Data scientist at Oracle
Verified email at oracle.com - Homepage
Cited by
Cited by
Adaptive knowledge sharing in multi-task learning: Improving low-resource neural machine translation
P Zaremoodi, W Buntine, G Haffari
Proceedings of the 56th Annual Meeting of the Association for Computational …, 2018
Novel class detection in data streams using local patterns and neighborhood graph
P ZareMoodi, H Beigy, SK Siahroudi
Neurocomputing 158, 234-245, 2015
Detection of evolving concepts in non-stationary data streams: A multiple kernel learning approach
SK Siahroudi, PZ Moodi, H Beigy
Expert systems with applications 91, 187-197, 2018
Neural machine translation for bilingually scarce scenarios: a deep multi-task learning approach
P Zaremoodi, G Haffari
arXiv preprint arXiv:1805.04237, 2018
A novel concept drift detection method in data streams using ensemble classifiers
M Dehghan, H Beigy, P ZareMoodi
Intelligent Data Analysis 20 (6), 1329-1350, 2016
Incorporating syntactic uncertainty in neural machine translation with forest-to-sequence model
P Zaremoodi, G Haffari
arXiv preprint arXiv:1711.07019, 2017
A support vector based approach for classification beyond the learned label space in data streams
P ZareMoodi, SK Siahroudi, H Beigy
Proceedings of the 31st Annual ACM Symposium on Applied Computing, 910-915, 2016
Concept-evolution detection in non-stationary data streams: a fuzzy clustering approach
P ZareMoodi, SK Siahroudi, H Beigy
Knowledge and Information Systems 60 (3), 1329-1352, 2019
Learning to Multi-Task Learn for Better Neural Machine Translation
P Zaremoodi, G Haffari
arXiv preprint arXiv:2001.03294, 2020
Adaptively Scheduled Multitask Learning: The Case of Low-Resource Neural Machine Translation
P Zaremoodi, G Haffari
Proceedings of the 3rd Workshop on Neural Generation and Translation, 177-186, 2019
Neural Machine Translation for Bilingually Low-Resource Scenarios
P Zaremoodi
Monash University, 2020
The system can't perform the operation now. Try again later.
Articles 1–11