3 papers
cs.LG2026
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…
cs.LG2026
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…
stat.CO2024
Scaling Up Bayesian Neural Networks with Neural Networks
Zahra Moslemi, Yang Meng, Shiwei Lan +1
Bayesian Neural Networks (BNNs) offer a principled and natural framework for proper uncertainty quantification in the context of deep learning. They address the typical challenges…