arXiv:2111.10722v4 Announce Type: replace-cross Abstract: We propose a novel deterministic sampling method, EVI-MMD, to approximate a target distribution $\rho^*$ by minimizing the kernel discrepancy, also known as the Maximum Mean Discrepancy (MMD). Leveraging the energetic variational inference framework (Wang et al., 2021), we transform the MMD minimization problem into solving a dynamic system of Ordinary Differential Equations (ODEs) for particles. The implicit Euler scheme is employed to solve the ODE system, leading to a proximal minimization problem at each iteration, which is efficien
The development of more efficient and deterministic sampling methods is a continuous pursuit within machine learning research, driven by the need for better statistical approximations in complex models.
Improved deterministic sampling methods can lead to more stable and efficient training of AI models, particularly in areas requiring accurate probabilistic inference and generative processes.
This research provides an alternative approach to traditional stochastic sampling, offering potential benefits in computational cost and convergence for certain applications.
- · AI researchers
- · Generative AI developers
- · Bayesian inference practitioners
- · Inefficient sampling methods
More robust and faster training for a subset of machine learning algorithms that rely on sampling.
Potential for broader application of complex probabilistic models in real-world scenarios due to improved computational tractability.
Acceleration of research into novel AI architectures and capabilities previously constrained by sampling limitations.
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