Kolmogorov-Arnold Energy Models: Fast, Interpretable Generative Modeling
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866909030331449344 |
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| author | Raj, Prithvi |
| author_facet | Raj, Prithvi |
| contents | Generative models typically rely on either simple latent priors (e.g., Variational Autoencoders, VAEs), which are efficient but limited, or highly expressive iterative samplers (e.g., Diffusion and Energy-based Models), which are costly and opaque. We introduce the Kolmogorov-Arnold Energy Model (KAEM) to bridge this trade-off and provide new opportunities for latent-space interpretability. Based on a novel adaptation of the Kolmogorov-Arnold Representation Theorem, KAEM imposes a univariate latent structure on the prior, enabling exact inference via the inverse transform method. With a low-dimensional latent space and appropriate inductive biases, importance sampling becomes a tractable, unbiased, and efficient posterior inference method. For settings where this fails, we propose a population-based strategy that decomposes the posterior into a sequence of annealed distributions, a new remedy for poor mixing in Energy-based Models. We compare KAEM against VAEs and the neural latent EBM architecture. KAEM attains the best Fréchet Inception Distance among latent-prior models on SVHN and CIFAR10, while sampling in a single forward pass and exposing an interpretable prior built from 1D densities. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14167 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Kolmogorov-Arnold Energy Models: Fast, Interpretable Generative Modeling Raj, Prithvi Machine Learning Generative models typically rely on either simple latent priors (e.g., Variational Autoencoders, VAEs), which are efficient but limited, or highly expressive iterative samplers (e.g., Diffusion and Energy-based Models), which are costly and opaque. We introduce the Kolmogorov-Arnold Energy Model (KAEM) to bridge this trade-off and provide new opportunities for latent-space interpretability. Based on a novel adaptation of the Kolmogorov-Arnold Representation Theorem, KAEM imposes a univariate latent structure on the prior, enabling exact inference via the inverse transform method. With a low-dimensional latent space and appropriate inductive biases, importance sampling becomes a tractable, unbiased, and efficient posterior inference method. For settings where this fails, we propose a population-based strategy that decomposes the posterior into a sequence of annealed distributions, a new remedy for poor mixing in Energy-based Models. We compare KAEM against VAEs and the neural latent EBM architecture. KAEM attains the best Fréchet Inception Distance among latent-prior models on SVHN and CIFAR10, while sampling in a single forward pass and exposing an interpretable prior built from 1D densities. |
| title | Kolmogorov-Arnold Energy Models: Fast, Interpretable Generative Modeling |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.14167 |