4 papers
Heterogeneous Graph Alignment for Joint Reasoning and Interpretability
Zahra Moslemi, Ziyi Liang, Norbert Fortin +1
Multi-graph learning is crucial for extracting meaningful signals from collections of heterogeneous graphs. However, effectively integrating information across graphs with differin…
Neural-Inspired Posterior Approximation (NIPA)
Babak Shahbaba, Zahra Moslemi
Humans learn efficiently from their environment by engaging multiple interacting neural systems that support distinct yet complementary forms of control, including model-based (goa…
Meta Fusion: A Unified Framework For Multimodality Fusion with Mutual Learning
Ziyi Liang, Annie Qu, Babak Shahbaba
Developing effective multimodal data fusion strategies has become increasingly essential for improving the predictive power of statistical machine learning methods across a wide ra…
Weakly-Supervised Multimodal Learning on MIMIC-CXR
Andrea Agostini, Daphné Chopard, Yang Meng +5
Multimodal data integration and label scarcity pose significant challenges for machine learning in medical settings. To address these issues, we conduct an in-depth evaluation of t…