State inference and parameter learning in sequential models can be
successfully performed with approximation techniques that maximize the evidence
lower bound to the marginal log-likelihood of the data distribution. These
methods may be referred to as Dynamical Variational Autoencoders, and our
specific focus lies on the deep Kalman filter. It has been shown that the ELBO
objective can oversimplify data representations, potentially compromising
estimation quality. Tighter Monte Carlo objectives have been proposed in the
literature to enhance generative modeling performance. For instance, the IWAE
objective uses importance weights to reduce the variance of marginal
log-likelihood estimates. In this paper, importance sampling is applied to the
DKF framework for learning deep Markov models, resulting in the IW-DKF, which
shows an improvement in terms of log-likelihood estimates and KL divergence
between the variational distribution and the transition model. The framework
using the sampled DKF update rule is also accommodated to address sequential
state and parameter estimation when working with highly non-linear
physics-based models. An experiment with the 3-space Lorenz attractor shows an
enhanced generative modeling performance and also a decrease in RMSE when
estimating the model parameters and latent states, indicating that tighter MCOs
lead to improved state inference performance.

在这篇论文中，通过重要性采样将序贯模型与 DKF 框架相结合，得到了 IW-DKF 方法，通过降低边际对数似然估计的方差，改进了生成建模性能和状态推断性能。

关于采样对深度序贯状态估计的影响

On the Impact of Sampling on Deep Sequential State Estimation

Human pose forecasting is an important problem in computer vision with
applications to human-robot interaction, visual surveillance, and autonomous
driving. Usually, forecasting algorithms use 3D skeleton sequences and are
trained to forecast for a few milliseconds into the future. Long-range
forecasting is challenging due to the difficulty of estimating how long a
person continues an activity. To this end, our contributions are threefold: (i)
we propose a generative framework for poses using variational autoencoders
based on Deep Markov Models (DMMs); (ii) we evaluate our pose forecasts using a
pose-based action classifier, which we argue better reflects the subjective
quality of pose forecasts than distance in coordinate space; (iii) last, for
evaluation of the new model, we introduce a 480,000-frame video dataset called
Ikea Furniture Assembly (Ikea FA), which depicts humans repeatedly assembling
and disassembling furniture. We demonstrate promising results for our approach
on both Ikea FA and the existing NTU RGB+D dataset.

本研究提出了一种基于深度马尔科夫模型的变分自编码器生成姿势的框架，探讨姿势预测的新方法，并基于一个床具拼装视频数据集进行验证。

基于深度马尔可夫模型的人体姿态预测

Human Pose Forecasting via Deep Markov Models

Many recent advances in large scale probabilistic inference rely on
variational methods. The success of variational approaches depends on (i)
formulating a flexible parametric family of distributions, and (ii) optimizing
the parameters to find the member of this family that most closely approximates
the exact posterior. In this paper we present a new approximating family of
distributions, the variational sequential Monte Carlo (VSMC) family, and show
how to optimize it in variational inference. VSMC melds variational inference
(VI) and sequential Monte Carlo (SMC), providing practitioners with flexible,
accurate, and powerful Bayesian inference. The VSMC family is a variational
family that can approximate the posterior arbitrarily well, while still
allowing for efficient optimization of its parameters. We demonstrate its
utility on state space models, stochastic volatility models for financial data,
and deep Markov models of brain neural circuits.

本文在变分推理中提出了一种新的逼近分布族：变分顺序蒙特卡罗（VSMC）族，并显示了如何在变分推理中优化它，从而将变分推理和顺序蒙特卡罗相结合，提供了灵活，准确且强大的贝叶斯推理。我们展示了它在状态空间模型，金融数据随机波动模型以及大脑神经电路的深度马尔可夫模型上的实用性。