From the 1 of 4 linked papers with an AI index.
4 papers
Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification
V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov
The paper proposes Kohn‑Sham Spectral Embedding (KSSE), a physics‑inspired, sparse‑graph spectral method that replaces dense CNN classifiers with a regularized Laplacian evaluated…
Diffusion-Guided Feature Selection via Nishimori Temperature: Noise-Based Spectral Embedding
Vasiliy S. Usatyuk, Denis A. Sapozhnikov, Sergey I. Egorov
We propose Noise-Based Spectral Embedding (NBSE), a physics-informed framework for selecting informative features from high-dimensional data without greedy search. NBSE constructs…
Natural Image Classification via Quasi-Cyclic Graph Ensembles and Random-Bond Ising Models at the Nishimori Temperature
V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov
Modern multi-class image classification uses high-dimensional CNN features that incur large memory and computational costs and obscure the data manifold's geometry. Existing graph-…
Synthetic Image Detection via Spectral Gaps of QC-RBIM Nishimori Bethe-Hessian Operators
V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov
The rapid advance of deep generative models such as GANs and diffusion networks now produces images that are virtually indistinguishable from genuine photographs, undermining media…