Sequential Monte Carlo (SMC) is an inference algorithm for state space models
that approximates the posterior by sampling from a sequence of target
distributions. The target distributions are often chosen to be the filtering
distributions, but these ignore information from future observations, leading
to practical and theoretical limitations in inference and model learning. We
introduce SIXO, a method that instead learns targets that approximate the
smoothing distributions, incorporating information from all observations. The
key idea is to use density ratio estimation to fit functions that warp the
filtering distributions into the smoothing distributions. We then use SMC with
these learned targets to define a variational objective for model and proposal
learning. SIXO yields provably tighter log marginal lower bounds and offers
significantly more accurate posterior inferences and parameter estimates in a
variety of domains.

SIXO 是一种结合了密度比估计和 SMC 算法的方法，用于学习逼近平滑分布的目标分布，从而在状态空间模型的推断和模型学习中获得更准确的后验推断和参数估计结果。

SIXO: 扭曲目标平滑推断

SIXO: Smoothing Inference with Twisted Objectives

In this work, we present and study a generalized family of differentiable
renderers. We discuss from scratch which components are necessary for
differentiable rendering and formalize the requirements for each component. We
instantiate our general differentiable renderer, which generalizes existing
differentiable renderers like SoftRas and DIB-R, with an array of different
smoothing distributions to cover a large spectrum of reasonable settings. We
evaluate an array of differentiable renderer instantiations on the popular
ShapeNet 3D reconstruction benchmark and analyze the implications of our
results. Surprisingly, the simple uniform distribution yields the best overall
results when averaged over 13 classes; in general, however, the optimal choice
of distribution heavily depends on the task.

本文研究了可微分渲染器相关的各个基础组件的需求，提出了一种广义的可微分渲染器，并利用不同的平滑分布进行了实例化，通过在 ShapeNet 3D 重建基准测试上的多种尝试，分析了结果的影响。令人惊讶的是，在 13 个类别中的平均结果上，简单的均匀分布效果最好；然而，通常来说，最佳的分布选择很大程度上取决于任务。

GenDR：通用可导渲染器

GenDR: A Generalized Differentiable Renderer

Computing smoothing distributions, the distributions of one or more states
conditional on past, present, and future observations is a recurring problem
when operating on general hidden Markov models. The aim of this paper is to
provide a foundation of particle-based approximation of such distributions and
to analyze, in a common unifying framework, different schemes producing such
approximations. In this setting, general convergence results, including
exponential deviation inequalities and central limit theorems, are established.
In particular, time uniform bounds on the marginal smoothing error are obtained
under appropriate mixing conditions on the transition kernel of the latent
chain. In addition, we propose an algorithm approximating the joint smoothing
distribution at a cost that grows only linearly with the number of particles.

本文研究了粒子方法逼近平滑分布问题，并提出线性时间复杂度的联合平滑分布的逼近算法。在该模型下，本文的主要贡献是建立了收敛性质和边界，证明了一些误差估计的一致性。