Formulating ensemble selection as mutual-information maximization reveals an information-theoretic error floor from model correlation and yields a greedy algorithm that outperforms baselines under fixed query budgets.
SENSE uses graph-based EEG encoding and semantic conditioning to synthesize speech from brain dynamics, outperforming baselines on acoustic and semantic metrics with minimal training subjects.
A VAE decomposes incomplete time-resolved Raman spectra into Raman signals, autofluorescence, and noise, outperforming prior methods on simulated and real data.