Published in Signal Processing, IEEE Transactions on, 2015
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Recommended citation: C. Soares, J. Xavier, J. Gomes, "Simple and Fast Convex Relaxation Method for Cooperative Localization in Sensor Networks Using Range Measurements." Signal Processing, IEEE Transactions on, 2015.
Published in IEEE Journal of Oceanic Engineering, 2017
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Recommended citation: Cl{\'a}udia Soares, Joao Gomes, Beatriz Ferreira, Joao Costeira, "LocDyn: Robust distributed localization for mobile underwater networks." IEEE Journal of Oceanic Engineering, 2017.
Published in Signal Processing, 2018
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Recommended citation: Beatriz Ferreira, Joao Gomes, Claudia Soares, Joao Costeira, "FLORIS and CLORIS: Hybrid source and network localization based on ranges and video." Signal Processing, 2018.
Published in IEEE Signal Processing Letters, 2020
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Recommended citation: C. {Soares}, F. {Valdeira}, J. {Gomes}, "Range and Bearing Data Fusion for Precise Convex Network Localization." IEEE Signal Processing Letters, 2020.
Published in arXiv preprint arXiv:2112.04355, 2021
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Recommended citation: Maria Carvalho, Claudia Soares, Pedro Louren{\c{c}}o, Rodrigo Ventura, "COSMIC: fast closed-form identification from large-scale data for LTV systems." arXiv preprint arXiv:2112.04355, 2021.
Published in IEEE Access, 2021
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Recommended citation: Filipa Valdeira, Ricardo Ferreira, Alessandra Micheletti, Claudia Soares, "From Noisy Point Clouds to Complete Ear Shapes: Unsupervised Pipeline." IEEE Access, 2021.
Published in GReS – Workshop on Graph Neural Networks for Recommendation and Search. Co-located with the ACM RecSys ’21 conference, 2021
Recommended citation: REGINA DUARTE, QIWEI HAN, CLAUDIA SOARES, "Referral prediction in Healthcare using Graph Neural Networks." GReS – Workshop on Graph Neural Networks for Recommendation and Search. Co-located with the ACM RecSys ’21 conference, 2021. https://europe.naverlabs.com/wp-content/uploads/2021/09/DuarteEtAl2021.pdf
Published in Signal Processing, 2021
Recommended citation: Cláudia Soares, João Gomes, "STRONG: Synchronous and asynchronous robust network localization, under non-Gaussian noise." Signal Processing, 2021. https://www.sciencedirect.com/science/article/pii/S0165168421001043
Published in IEEE Transactions on Intelligent Transportation Systems, 2022
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Recommended citation: Marta Guimaraes, Cl{\'a}udia Soares, Rodrigo Ventura, "Decision Support Models for Predicting and Explaining Airport Passenger Connectivity From Data." IEEE Transactions on Intelligent Transportation Systems, 2022.
Published in arXiv preprint arXiv:2207.08993, 2022
This survey describes the background and motivation for the research on the Orbit prediction problem. It discusses the increasing population of human-made objects in space and the need for fast and reliable methods for space situational awareness, maneuver recommendation, and planning. The text highlights the limitations of current physics-only methods and the potential of data-driven techniques, specifically machine learning, to improve orbit prediction accuracy. It mentions the challenges related to uncertainties in the state of space objects, environmental conditions, and unknown characteristics. The text also mentions the importance of interdisciplinary research in space sciences and machine learning for physical systems. This paper provides an overview of the current work in space debris tracking and orbit prediction, emphasizing the potential of data-driven techniques, particularly machine learning, to enhance orbit prediction accuracy. It highlights the challenges associated with uncertainties and unknown characteristics of space objects and discusses the abstraction capacity of deep learning models in modeling complex non-linear systems.
Recommended citation: Francisco Caldas, Claudia Soares, "Machine Learning in Orbit Estimation: a Survey." arXiv preprint arXiv:2207.08993, 2022.
Published in IEEE Transactions on Signal Processing, 2022
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Recommended citation: Joao Domingos, Claudia Soares, Joao Xavier, "Robust Localization With Bounded Noise: Creating a Superset of the Possible Target Positions Via Linear-Fractional Representations." IEEE Transactions on Signal Processing, 2022.
Published in Submitted, 2022
This survey describes the background and motivation for the research on the Orbit prediction problem.
Recommended citation: Caldas, Francisco, and Cláudia Soares. "Machine Learning in Orbit Estimation: a Survey.", arXiv preprint arXiv:2207.08993 (2022). https://arxiv.org/abs/2207.08993
Published in Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, 2022
Emergency Department overcrowding is driven, in part, by non-urgent patients that resort to emergency services despite not having a medical emergency, representing almost half of the total number of daily patients. This paper describes a novel deep learning architecture, the Temporal Fusion Transformer, that uses calendar and time-series covariates to forecast prediction intervals and point predictions for a 4-week period. We have concluded that patient volume can be forecast with a Mean Absolute Percentage Error (MAPE) of 9.87%.
Recommended citation: Caldas, Francisco M., and Cláudia Soares. "A Temporal Fusion Transformer for Long-term Explainable Prediction of Emergency Department Overcrowding." Machine Learning and Principles and Practice of Knowledge Discovery in Databases: International Workshops of ECML PKDD 2022, Grenoble, France, September 19–23, 2022, Proceedings, Part I. Cham: Springer Nature Switzerland. https://arxiv.org/pdf/2207.00610.pdf
Published in arXiv preprint arXiv:2309.09977, 2023
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Recommended citation: Pedro Valdeira, Yuejie Chi, Claudia Soares, Jo{\~a}o Xavier, "A Multi-Token Coordinate Descent Method for Semi-Decentralized Vertical Federated Learning." arXiv preprint arXiv:2309.09977, 2023.
Published in Optimization Letters, 2023
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Recommended citation: Stevo Rackovic, Cl{\'a}udia Soares, Du{\v{s}}an Jakoveti{\'c}, Zoranka Desnica, "A majorization--minimization-based method for nonconvex inverse rig problems in facial animation: algorithm derivation." Optimization Letters, 2023.
Published in Journal of Space Safety Engineering, 2023
Recommended citation: Chiara Manfletti, Marta Guimaraes, Claudia Soares, "AI for space traffic management." Journal of Space Safety Engineering, 2023. https://www.sciencedirect.com/science/article/pii/S2468896723000897
Published in arXiv preprint arXiv:2302.04843, 2023
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Recommended citation: Stevo Rackovic, Cl{\'a}udia Soares, Du{\v{s}}an Jakoveti{\'c}, Zoranka Desnica, "Accurate and Interpretable Solution of the Inverse Rig for Realistic Blendshape Models with Quadratic Corrective Terms." arXiv preprint arXiv:2302.04843, 2023.
Published in arXiv preprint arXiv:2308.11022, 2023
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Recommended citation: Filipa Valdeira, Stevo Rackovi{\'c}, Valeria Danalachi, Qiwei Han, Cl{\'a}udia Soares, "Extreme Multilabel Classification for Specialist Doctor Recommendation with Implicit Feedback and Limited Patient Metadata." arXiv preprint arXiv:2308.11022, 2023.
Published in arXiv preprint arXiv:2302.04820, 2023
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Recommended citation: Stevo Rackovic, Cl{\'a}udia Soares, Du{\v{s}}an Jakoveti{\'c}, Zoranka Desnica, "High-fidelity Interpretable Inverse Rig: An Accurate and Sparse Solution Optimizing the Quartic Blendshape Model." arXiv preprint arXiv:2302.04820, 2023.
Published in arXiv preprint arXiv:2306.07809, 2023
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Recommended citation: Diogo Lavado, Cl{\'a}udia Soares, Alessandra Micheletti, Giovanni Bocchi, Alex Coronati, Manuel Silva, Patrizio Frosini, "Low-Resource White-Box Semantic Segmentation of Supporting Towers on 3D Point Clouds via Signature Shape Identification." arXiv preprint arXiv:2306.07809, 2023.
Published in Accepted at the IEEE Aerospace Conference, 2023
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Recommended citation: Ricardo Ferreira, Marta Guimaraes, Filipa Valdeira, Claudia Soares, "One-Shot Initial Orbit Determination in Low-Earth Orbit." Accepted at the IEEE Aerospace Conference, 2023.
Published in Accepted in the International Astronautical Congress 2023, 2023
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Recommended citation: Marta Guimaraes, Claudia Soares, Chiara Manfletti, "Predicting the Position Uncertainty at the Time of Closest Approach with Diffusion Models." Accepted in the International Astronautical Congress 2023, 2023.
Published in Accepted in the International Astronautical Congress 2023, 2023
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Recommended citation: Marta Guimaraes, Claudia Soares, Chiara Manfletti, "Taxonomy for Resident Space Objects in LEO." Accepted in the International Astronautical Congress 2023, 2023.
Published in SIAM Journal on Mathematics of Data Science, 2023
We propose a method for fitting/registering shapes with missing data using Gaussian processes. Gaussian processes provide a powerful tool for shape modelling and fitting. However, existing methods in this area do not work well for detailed and deformed data with extensive missing data, such as ears. To address this, we use a multi-annotator Gaussian process regression to formulate the shape fitting problem and establish a parallel with standard probabilistic registration. Our method, called the shape fitting Gaussian process (SFGP), outperforms current approaches for registration with GP and a state-of-the-art registration method when dealing with extensive missing data. We conducted experiments on both a small 2D dataset with several transformations and a 3D dataset of ears.
Recommended citation: Valdeira, F. M., Ferreira, R., Micheletti, A., & Soares, C. (2023). Probabilistic Registration for Gaussian Process Three-Dimensional Shape Modelling in the Presence of Extensive Missing Data. SIAM Journal on Mathematics of Data Science, 5(2), 502-527.
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MSc course, NOVA School of Science and Technology, Department of Computer Science, 2022
The MSc machine learning course is a rigorous and comprehensive program designed to equip students with advanced knowledge and skills in machine learning. With a focus on both theoretical foundations and practical applications, this course offers students a deep understanding of the algorithms, models, and techniques used in machine learning.
MSc course, NOVA School of Science and Technology, Department of Computer Science, 2023
Just a few years ago, there was no abundance of deep tech companies, large and small, where deep learning specialists (mostly with PhDs) develop incredible, practical systems that are widely used. These systems cover a variety of research areas, such as computer vision, natural language processing, and reinforcement learning — topics that we’ll cover in this course. The success of deep learning has even inspired research in theoretical learning theory and mathematics. These tools are already having a major influence on industry and society, revolutionizing how films are made, machines are designed, and playing a larger role in fundamental sciences — from astrophysics to medicine.