We introduce Hyper-Conditioned Neural Autoregressive Flow (HCNAF); a powerful
universal distribution approximator designed to model arbitrarily complex
conditional probability density functions. HCNAF consists of a neural-net based
conditional autoregressive flow (AF) and a hyper-network that can take large
conditions in non-autoregressive fashion and outputs the network parameters of
the AF. Like other flow models, HCNAF performs exact likelihood inference. We
conduct a number of density estimation tasks on toy experiments and MNIST to
demonstrate the effectiveness and attributes of HCNAF, including its
generalization capability over unseen conditions and expressivity. Finally, we
show that HCNAF scales up to complex high-dimensional prediction problems of
the magnitude of self-driving and that HCNAF yields a state-of-the-art
performance in a public self-driving dataset.

本论文介绍了一种名为 “超条件神经自回归流”(HCNAF) 的通用机器学习模型，它可以对任意复杂的条件概率密度函数进行建模。作者对该模型进行了密度估计实验，并在公共的自动驾驶数据集上进行了试验，结果表明该模型表现优异。

HCNAF: 超条件神经自回归流及其在概率占用地图预测中的应用

HCNAF: Hyper-Conditioned Neural Autoregressive Flow and its Application  for Probabilistic Occupancy Map Forecasting

Monotonic neural networks have recently been proposed as a way to define
invertible transformations. These transformations can be combined into powerful
autoregressive flows that have been shown to be universal approximators of
continuous probability distributions. Architectures that ensure monotonicity
typically enforce constraints on weights and activation functions, which
enables invertibility but leads to a cap on the expressiveness of the resulting
transformations. In this work, we propose the Unconstrained Monotonic Neural
Network (UMNN) architecture based on the insight that a function is monotonic
as long as its derivative is strictly positive. In particular, this latter
condition can be enforced with a free-form neural network whose only constraint
is the positiveness of its output. We evaluate our new invertible building
block within a new autoregressive flow (UMNN-MAF) and demonstrate its
effectiveness on density estimation experiments. We also illustrate the ability
of UMNNs to improve variational inference.

该研究提出了一种无限制单调神经网络（UMNN）架构，基于函数导数严格为正，进而在自回归流中实现密度估计实验，并展示了 UMNN 提高变分推断的能力。

无约束单调神经网络

Unconstrained Monotonic Neural Networks

We present Sequential Neural Likelihood (SNL), a new method for Bayesian
inference in simulator models, where the likelihood is intractable but
simulating data from the model is possible. SNL trains an autoregressive flow
on simulated data in order to learn a model of the likelihood in the region of
high posterior density. A sequential training procedure guides simulations and
reduces simulation cost by orders of magnitude. We show that SNL is more
robust, more accurate and requires less tuning than related neural-based
methods, and we discuss diagnostics for assessing calibration, convergence and
goodness-of-fit.

本研究提出了顺序神经可能性方法（SNL），这是一种用于贝叶斯推断的新方法，适用于具有难以计算可能性的模拟器模型，其中可以从模型中模拟数据。SNL 在模拟数据上训练自回归流，以学习高后验密度区域可能性的模型。该论文表明，相对于相关的基于神经网络方法，SNL 更加健壮，更准确并且需要较少的调整，并且讨论了用于评估校准，收敛和拟合优度的诊断方法。