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cs.LG2026
Boltzmann Attention: Learnable Ising Couplings for Cooperative Attention
Gilhan Kim, Daniel K. Park
Attention mechanisms are central to modern sequence models, yet standard attention computes relevance primarily through individual query--key similarities. Although softmax normali…
quant-ph2026
Multi-Mode Quantum Annealing for Generative Representation Learning with Boltzmann Priors
Gilhan Kim, Daniel K. Park
Energy-based models provide a natural bridge between statistical physics and machine learning by representing data through structured energy landscapes. Boltzmann machines are a pa…
stat.ML2026
Information-Geometric Decomposition of Generalization Error in Unsupervised Learning
Gilhan Kim
We decompose the Kullback--Leibler generalization error (GE) -- the expected KL divergence from the data distribution to the trained model -- of unsupervised learning into three no…