Blind source separation (BSS) recovers latent sources from observed mixtures. In brain imaging, sources occur in two domains, spatial and temporal, so a criterion applied in one leaves the other unconstrained. Independent component analysis, the most used BSS tool, exploits diversity such as non-Gaussianity and sample...
Causal discovery seeks to determine directional relationships among variables in complex systems, yet observational data alone generally do not identify causal direction. Complete identification therefore requires assumptions beyond linearity and acyclicity. A key approach is the Linear Non-Gaussian Acyclic Model (LiNG...
An adaptive layerwise alignment module that progressively aligns and fuses features from imaging branches through attention-driven interactions at multiple network layers, enabling continuous and sufficient cross-modality information exchange and a tabular-adaptive parameterization module that incorporates structured c...
Chuang Liang, Tülay Adalı, Jing Sui et al.· Proceedings of the 32nd ACM...· 0 citations
This work presents BrainNetGFM, a novel graph-based foundation model for brain network construction that integrates individualized geometry with joint self-supervised learning, pre-trained on a massive dataset of 44,476 samples and proposes a joint self-supervised learning framework that synergizes generative and contr...
Chunzhi Zhao, Tülay Adalı, Jing Sui et al.· Proceedings of the 32nd ACM...· 0 citations
This work introduces a spatiotemporal variational tensor decomposition (ST-VTD) framework that combines a tensor factorization generative model with structured priors to jointly represent spatial maps and temporal dynamics and significantly improves latent factor recovery.
L. Montaldo, R. Borsoi, Sebastian Miron et al.· arXiv.org· 0 citations
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