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Panagiotis Lymperopoulos
Panagiotis Lymperopoulos
PhD Candidate, Tufts University
Verified email at tufts.edu
Title
Cited by
Cited by
Year
Branching principles of animal and plant networks identified by combining extensive data, machine learning and modelling
AB Brummer, P Lymperopoulos, J Shen, E Tekin, LP Bentley, V Buzzard, ...
Journal of the Royal Society Interface 18 (174), 20200624, 2021
142021
Concept wikification for covid-19
P Lymperopoulos, H Qiu, B Min
Proceedings of the 1st Workshop on NLP for COVID-19 (Part 2) at EMNLP 2020, 2020
72020
NovelCraft: A Dataset for Novelty Detection and Discovery in Open Worlds
P Feeney, S Schneider, P Lymperopoulos, L Liu, M Scheutz, MC Hughes
arXiv preprint arXiv:2206.11736, 2022
62022
Exploiting Variable Correlation with Masked Modeling for Anomaly Detection in Time Series
P Lymperopoulos, Y Li, L Liu
NeurIPS 2022 Workshop on Robustness in Sequence Modeling, 2022
32022
A neurosymbolic cognitive architecture framework for handling novelties in open worlds
S Goel, P Lymperopoulos, R Thielstrom, E Krause, P Feeney, P Lorang, ...
Artificial Intelligence 331, 104111, 2024
22024
Integrating Planning, Execution and Monitoring in the presence of Open World Novelties: Case Study of an Open World Monopoly Solver
S Gopalakrishnan, U Soni, T Thai, P Lymperopoulos, M Scheutz, ...
arXiv preprint arXiv:2107.04303, 2021
12021
Forecasting COVID-19 Counts At A Single Hospital: A Hierarchical Bayesian Approach
AH Lee, P Lymperopoulos, JT Cohen, JB Wong, MC Hughes
arXiv preprint arXiv:2104.09327, 2021
12021
Identifying branching principles in biological networks using imaging, modeling, and machine learning
AB Brummer, P Lymperopoulos, J Shen, E Tekin, LP Bentley, V Buzzard, ...
arXiv preprint arXiv:1903.04642, 2019
12019
Graph Pruning for Enumeration of Minimal Unsatisfiable Subsets
P Lymperopoulos, L Liu
arXiv preprint arXiv:2402.15524, 2024
2024
NovelGym: A Flexible Ecosystem for Hybrid Planning and Learning Agents Designed for Open Worlds
S Goel, Y Wei, P Lymperopoulos, M Scheutz, J Sinapov
arXiv preprint arXiv:2401.03546, 2024
2024
Deep-learning-based image restoration of depth-resolved, label-free, two-photon images for the quantitative morphological and functional characterization of human cervical tissues
CM Polleys, P Lymperopoulos, HT Thieu, E Genega, L Liu, I Georgakoudi
Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XIX …, 2021
2021
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