3 citations · 3 across the 7 of their papers we have counts for
9 papers
Multi-Label Proportion Learning for Sea-Ice Type Prediction
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data for this task is the ice chart…
Uncertainty-Aware Learning from Multi-Expert Interval Targets
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Many machine learning (ML) applications rely on expert labels, and qualified experts may provide different but plausible interpretations of the same observation. Such variation acr…
Uncertainty-Aware Sea-Ice Type Mapping with Multiple Ice Charts
Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett +1
Sea-ice stage of development (SoD) describes the age and associated thickness of sea ice and provides important information for navigation, and operational ice monitoring. SoD labe…
An Autonomous GeoAI Agent for Arctic Eco-Navigation
Samira Alkaee Taleghan, Younghyun Koo, Farnoush Banaei-Kashani
Arctic maritime navigation is becoming increasingly important as changing sea-ice conditions expand seasonal accessibility while simultaneously introducing substantial operational,…
TCA-SIR: Learning Target-Conditioned Abstractions for Scientific Inspiration Retrieval
Yuto Suzuki, Farnoush Banaei-Kashani
Scientific hypothesis generation for AI for Science typically involves Scientific Inspiration Retrieval (SIR) followed by hypothesis composition. Existing SIR methods rank papers b…
BioNeuralNet: A Graph Neural Network based Multi-Omics Network Data Analysis Tool
Vicente Ramos, Sundous Hussein, Mohamed Abdel-Hafiz +6
Multi-omics data offer unprecedented insights into complex biological systems, yet their high dimensionality, sparsity, and intricate interactions pose significant analytical chall…
IceBench: A Benchmark for Deep Learning based Sea Ice Type Classification
Samira Alkaee Taleghan, Andrew P. Barrett, Walter N. Meier +1
Sea ice plays a critical role in the global climate system and maritime operations, making timely and accurate classification essential. However, traditional manual methods are tim…
Ice-FMBench: A Foundation Model Benchmark for Sea Ice Type Segmentation
Samira Alkaee Taleghan, Morteza Karimzadeh, Andrew P. Barrett +2
Accurate segmentation and mapping of sea ice types is crucial for safe polar navigation, offshore operations, and climate monitoring. While deep learning has demonstrated strong po…
Partial Label Learning with Focal Loss for Sea Ice Classification Based on Ice Charts
Behzad Vahedi, Benjamin Lucas, Farnoush Banaei-Kashani +4
Sea ice, crucial to the Arctic and Earth's climate, requires consistent monitoring and high-resolution mapping. Manual sea ice mapping, however, is time-consuming and subjective, p…