21 papers
AdaptICA: Data-Adaptive Transformation Learning for Independent Component Analysis
Lida Jalili, Jingyu Liu, Vince D. Calhoun +1
Independent component analysis (ICA) is widely used to recover latent structure from signal and imaging data, but standard ICA assumes that the observed measurement scale preserves…
Latent graph encoding of multimodal neuroimaging features with generative AI architectures
Ishaan Batta, Meenu Ajith, Vince Calhoun
While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encodin…
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
Dasen Dai, Yanteng Zhang, Shuoqi Li +6
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as…
Sparse Deep Additive Model with Interactions: Enhancing Interpretability and Predictability
Yi-Ting Hung, Li-Hsiang Lin, Vince D. Calhoun
Recent advances in deep learning highlight the need for personalized models that can learn from small samples, handle high-dimensional features, and remain interpretable. To addres…
Timescale Coalescence Makes Hidden Persistent Forcing Spectrally Dark
Yuda Bi, Chenyu Zhang, Vince D Calhoun
Under coarse observation, unresolved slow forcing can remain dynamically active yet locally invisible to reduced spectral inference. For a solvable driven AR benchmark, the lo…
Scaling Laws are Redundancy Laws
Yuda Bi, Vince D Calhoun
Scaling laws, a defining feature of deep learning, reveal a striking power-law improvement in model performance with increasing dataset and model size. Yet, their mathematical orig…