3 citations · 4 across the 19 of their papers we have counts for
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Kohn-Sham Spectral Embedding on Sparse Graphs at the Nishimori Temperature for Image Classification
V. S. Usatyuk, D. A. Sapozhnikov, S. I. Egorov
We propose Kohn-Sham Spectral Embedding (KSSE), an energy-based model replacing the top-layer classifier of convolutional networks with a sparse-graph spectral embedding at the Nis…
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-…