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cs.LG2026
Disentanglement with Holographic Reduced Representations
Jhonny J. Velasquez Olivera, Christo K. Thomas, Walid Saad
Disentanglement, the separation of factors of variation in data using neural networks, remains a long-standing challenge in machine learning. Prior work has addressed this problem…
cs.LG2026
Toward World Models for Epidemiology
Zeeshan Memon, Yiqi Su, Christo Kurisummoottil Thomas +3
World models have emerged as a unifying paradigm for learning latent dynamics, simulating counterfactual futures, and supporting planning under uncertainty. In this paper, we argue…