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This paper introduces Neural Optimal Particle Filters (NOPFs), which learn amortized proposal distributions to improve sample efficiency and distributional accuracy in sequential inference tasks, enhancing classical particle filters with data-driven methods.
This paper introduces Hybrid Probabilistic Zonotopes (HProbZ), a neural network output head that jointly models discrete modes, bounded drift, and stochastic noise with closed-form likelihood, enabling identifiable uncertainty decomposition, observation-driven contraction, and multi-modal conformal coverage.
This position paper argues that probabilistic scaling of generative models is insufficient for quantum circuit generation due to strict mathematical constraints, and proposes verification-aware architectures that integrate formal methods.