We introduce neural particle smoothing, a sequential Monte Carlo method for
sampling annotations of an input string from a given probability model. In
contrast to conventional particle filtering algorithms, we train a proposal
distribution that looks ahead to the end of the input string by means of a
right-to-left LSTM. We demonstrate that this innovation can improve the quality
of the sample. To motivate our formal choices, we explain how our neural model
and neural sampler can be viewed as low-dimensional but nonlinear
approximations to working with HMMs over very large state spaces.

本文介绍了神经粒子平滑法，一种用于从给定概率模型中采样输入字符串注释的顺序蒙特卡罗方法，通过训练向前看的 LSTM 来改进样本的质量，同时解释了神经模型和神经采样器可被视为处理非常大状态空间上的 HMM 时的低维非线性逼近。

用神经粒子平滑法从条件序列模型中进行采样

Neural Particle Smoothing for Sampling from Conditional Sequence Models

Superoptimization requires the estimation of the best program for a given
computational task. In order to deal with large programs, superoptimization
techniques perform a stochastic search. This involves proposing a modification
of the current program, which is accepted or rejected based on the improvement
achieved. The state of the art method uses uniform proposal distributions,
which fails to exploit the problem structure to the fullest. To alleviate this
deficiency, we learn a proposal distribution over possible modifications using
Reinforcement Learning. We provide convincing results on the superoptimization
of "Hacker's Delight" programs.

该论文介绍了使用强化学习学习建议分布来提高超级优化技术中的随机搜索方法，该方法改进了当前程序的修改建议，通过解决程序修改建议的优化问题可用于压缩程序，提高程序速度及减小程序文件大小。

程序超优化的学习 - 工作坊版

Learning to superoptimize programs - Workshop Version

Sequential Monte Carlo (SMC), or particle filtering, is a popular class of
methods for sampling from an intractable target distribution using a sequence
of simpler intermediate distributions. Like other importance sampling-based
methods, performance is critically dependent on the proposal distribution: a
bad proposal can lead to arbitrarily inaccurate estimates of the target
distribution. This paper presents a new method for automatically adapting the
proposal using an approximation of the Kullback-Leibler divergence between the
true posterior and the proposal distribution. The method is very flexible,
applicable to any parameterized proposal distribution and it supports online
and batch variants. We use the new framework to adapt powerful proposal
distributions with rich parameterizations based upon neural networks leading to
Neural Adaptive Sequential Monte Carlo (NASMC). Experiments indicate that NASMC
significantly improves inference in a non-linear state space model
outperforming adaptive proposal methods including the Extended Kalman and
Unscented Particle Filters. Experiments also indicate that improved inference
translates into improved parameter learning when NASMC is used as a subroutine
of Particle Marginal Metropolis Hastings. Finally we show that NASMC is able to
train a latent variable recurrent neural network (LV-RNN) achieving results
that compete with the state-of-the-art for polymorphic music modelling. NASMC
can be seen as bridging the gap between adaptive SMC methods and the recent
work in scalable, black-box variational inference.

本文介绍了一种新的适应性 SMD 方法，该方法使用了 Kullback-Leibler 散度的近似来自动适应提议分布，该方法非常灵活，适用于任何参数化的提议分布，并支持在线和批处理变量，我们使用新框架来适应基于神经网络的强大提议分布，从而导致神经自适应序贯蒙特卡洛，实验表明，在非线性状态空间模型中，NASMC 显着改善了推断，优于自适应提议方法，包括扩展卡尔曼和无味粒子滤波器，也表明当 NASMC 用作粒子分辨率 Metropolis　Hastings 的子程序时，改进的推断可转化为参数学习的改进。最后，我们展示 NASMC 能够训练潜在变量递归神经网络（LV-RNN），达到与多态音乐建模的最新水平竞争的结果。NASMC 可以看作是将自适应 SMC 方法与最近的可扩展的黑箱变分推理工作之间的桥梁。

神经自适应序贯蒙特卡罗

Neural Adaptive Sequential Monte Carlo

We propose nested sequential Monte Carlo (NSMC), a methodology to sample from
sequences of probability distributions, even where the random variables are
high-dimensional. NSMC generalises the SMC framework by requiring only
approximate, properly weighted, samples from the SMC proposal distribution,
while still resulting in a correct SMC algorithm. Furthermore, NSMC can in
itself be used to produce such properly weighted samples. Consequently, one
NSMC sampler can be used to construct an efficient high-dimensional proposal
distribution for another NSMC sampler, and this nesting of the algorithm can be
done to an arbitrary degree. This allows us to consider complex and
high-dimensional models using SMC. We show results that motivate the efficacy
of our approach on several filtering problems with dimensions in the order of
100 to 1 000.

本文提出了嵌套顺序蒙特卡罗 (NSMC) 方法，可以采样高维的概率分布，扩展了 SMC 框架并被用于复杂的高维模型和滤波问题中。