Approximate-message-passing methods offer fast Bayesian inference for high-dimensional inverse problems, but their performance and state-evolution predictions rely on correctly specified module parameters. This paper develops an adaptive version of score-based vector approximate message passing (SC-VAMP). Each parameterized factor is updated by a local tilted expectation-maximization (EM) step that reuses the tilted moments already computed by the single-input single-output module interface. Under standard large-system state-evolution assumptions and identifiability conditions, the matched parameters form a Bayes-optimal population fixed point of the adaptive recursion. The argument is written separately for prior modules and likelihood/LMMSE modules, the latter using the Gaussian cavity induced by the VAMP transformed-error model. Numerical results for linear and one-bit Bernoulli-Gaussian compressed sensing show that the proposed updates recover near-oracle performance from strongly mismatched initializations.
Coarsening-based training for graph neural networks (GNNs), i.e.\ training on coarsened graphs rather than the original large ones, has become a promising direction for scaling GNNs to massive graphs. However, prior work has been evaluated almost exclusively on \textit{homophilic…
Rydberg atomic receiver has emerged as promising candidate for next-generation wireless communication, due to the exceptional sensitivity and ability to overcome the physical limitations of traditional radio frequency antennas. Utilizing the resonant response of atomic energy lev…
Radio maps, which estimate spatial radio-frequency characteristics from spectrum measurements, are essential for applications such as spectrum management and network planning. With the continuous arrival of spectrum measurements, conventional batch processing methods for radio ma…
In this study, a novel encoder-only Transformer-based receiver architecture is presented for BPSK signals transmitted over Faster-than-Nyquist (FTN) signaling channels that introduce intentional inter-symbol interference (ISI) with a compression factor of $τ=0.8$. A complete end-…
Chronic respiratory diseases affect hundreds of millions of people worldwide, with spirometry serving as the gold standard for pulmonary function assessment. However, conventional spirometry's reliance on mouthpieces and nose clips creates discomfort and technical challenges that…
Deep learning for EEG-based seizure detection faces critical challenges: severe annotation scarcity and extreme class imbalance, where ictal events comprise less than 10\% of clinical recordings. We present DiffEEG, a 9.6M-parameter self-supervised foundation model that addresses…