Tensor Train Diffusion: Leveraging Low-Rank Structures for High-Dimensional Score-Based Sampling
arXiv:2607.06841v1 Announce Type: cross Abstract: Diffusion models offer a powerful framework for sampling from complex probability densities by learning to reverse a noising process. A common approach involves solving for the time-reversed stochastic differential equation (SDE), which requires the score function of the evolving sample distribution. The logarithm of this distribution's density is governed by a Hamilton-Jacobi-Bellman (HJB) type partial differential equation (PDE). However, current methods for solving this PDE, such as PINNs or trajectory-based techniques, often suffer from lon
This paper addresses a known computational bottleneck in high-dimensional diffusion models, a core component of modern AI systems, indicating ongoing efforts to scale these technologies.
Improving the efficiency of high-dimensional score-based sampling can unlock more complex and efficient AI models, impacting various downstream applications from generative AI to scientific discovery.
This research suggests a potential pathway to overcome limitations in current diffusion models, enabling the development of models that can handle significantly higher data dimensionality with reduced computational cost.
- · AI compute providers
- · Generative AI developers
- · Machine learning researchers
- · High-dimensional data scientists
- · Computational approaches reliant on less efficient sampling methods
More efficient diffusion models become feasible for complex tasks.
Reduced computational cost for training and inference in certain AI applications.
Acceleration of research and development in areas requiring high-dimensional data processing and generation, potentially impacting drug discovery or materials science.
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