79 citations · 332 across the 27 of their papers we have counts for
13 papers · 1 filter
Does Latent Context Help? A Controlled Evaluation of Inverse Reinforcement Learning in Arctic Shipping
Vaishnav Vaidheeswaran, Dilith Jayakody, Biruk Ambaw +3
Artificial Intelligence (AI)-assisted navigation can help Arctic shipping adapt to rapidly changing sea-ice conditions, but reliable deployment requires reward models that are inte…
Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling
Parth Doshi, Priyanka Aravindan, Vaishnav Vaidheeswaran +2
Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure. For sea s…
Covariance Last-Layer Ensembles: Function-Space Diversity for Efficient Uncertainty Quantification
H. Martin Gillis, Isaac Xu, Gabriel Spadon +1
A Last-Layer Ensemble (LLE), linear units on one shared frozen feature map, is an efficient single-pass approach to the disagreement-based epistemic uncertainty for out-of-dist…
Goal-Conditioned Reinforcement Learning for Data-Driven Maritime Navigation
Vaishnav Vaidheeswaran, Dilith Jayakody, Samruddhi Mulay +3
Routing vessels through narrow and dynamic waterways is challenging due to changing environmental conditions and operational constraints. Existing vessel-routing studies typically…
Physics-Informed Neural Networks for Vessel Trajectory Prediction: Learning Time-Discretized Kinematic Dynamics via Finite Differences
Md Mahbub Alam, Amilcar Soares, José F. Rodrigues-Jr +1
Accurate vessel trajectory prediction is crucial for navigational safety, route optimization, traffic management, search and rescue operations, and autonomous navigation. Tradition…
ImPORTance: Machine Learning-Driven Analysis of Global Port Significance and Network Dynamics for Improved Operational Efficiency
Emanuele Carlini, Domenico Di Gangi, Vinicius Monteiro de Lira +3
Seaports play a crucial role in the global economy, and researchers have sought to understand their significance through various studies. In this paper, we aim to explore the commo…
Enhancing Global Maritime Traffic Network Forecasting with Gravity-Inspired Deep Learning Models
Ruixin Song, Gabriel Spadon, Ronald Pelot +2
Aquatic non-indigenous species (NIS) pose significant threats to biodiversity, disrupting ecosystems and inflicting substantial economic damages across agriculture, forestry, and f…
Multi-Path Long-Term Vessel Trajectories Forecasting with Probabilistic Feature Fusion for Problem Shifting
Gabriel Spadon, Jay Kumar, Derek Eden +6
This paper addresses the challenge of boosting the precision of multi-path long-term vessel trajectory forecasting on engineered sequences of Automatic Identification System (AIS)…
A semi-supervised methodology for fishing activity detection using the geometry behind the trajectory of multiple vessels
Martha Dais Ferreira, Gabriel Spadon, Amilcar Soares +1
Automatic Identification System (AIS) messages are useful for tracking vessel activity across oceans worldwide using radio links and satellite transceivers. Such data plays a signi…
From Cities to Series: Complex Networks and Deep Learning for Improved Spatial and Temporal Analytics*
Gabriel Spadon, Jose F. Rodrigues-Jr
Graphs have often been used to answer questions about the interaction between real-world entities by taking advantage of their capacity to represent complex topologies. Complex net…
Unfolding AIS transmission behavior for vessel movement modeling on noisy data leveraging machine learning
Gabriel Spadon, Martha D. Ferreira, Amilcar Soares +1
The oceans are a source of an impressive mixture of complex data that could be used to uncover relationships yet to be discovered. Such data comes from the oceans and their surface…
Pay Attention to Evolution: Time Series Forecasting with Deep Graph-Evolution Learning
Gabriel Spadon, Shenda Hong, Bruno Brandoli +3
Time-series forecasting is one of the most active research topics in artificial intelligence. Applications in real-world time series should consider two factors for achieving relia…
Patient trajectory prediction in the Mimic-III dataset, challenges and pitfalls
Jose F Rodrigues-Jr, Gabriel Spadon, Bruno Brandoli +1
Automated medical prognosis has gained interest as artificial intelligence evolves and the potential for computer-aided medicine becomes evident. Nevertheless, it is challenging to…