Fast and automated inference of binary-lens, single-source (2L1S)
microlensing events with sampling-based Bayesian algorithms (e.g., Markov Chain
Monte Carlo; MCMC) is challenged on two fronts: high computational cost of
likelihood evaluations with microlensing simulation codes, and a pathological
parameter space where the negative-log-likelihood surface can contain a
multitude of local minima that are narrow and deep. Analysis of 2L1S events
usually involves grid searches over some parameters to locate approximate
solutions as a prerequisite to posterior sampling, an expensive process that
often requires human-in-the-loop domain expertise. As the next-generation,
space-based microlensing survey with the Roman Space Telescope is expected to
yield thousands of binary microlensing events, a new fast and automated method
is desirable. Here, we present a likelihood-free inference (LFI) approach named
amortized neural posterior estimation, where a neural density estimator (NDE)
learns a surrogate posterior $\hat{p}(\theta|x)$ as an observation-parametrized
conditional probability distribution, from pre-computed simulations over the
full prior space. Trained on 291,012 simulated Roman-like 2L1S simulations, the
NDE produces accurate and precise posteriors within seconds for any observation
within the prior support without requiring a domain expert in the loop, thus
allowing for real-time and automated inference. We show that the NDE also
captures expected posterior degeneracies. The NDE posterior could then be
refined into the exact posterior with a downstream MCMC sampler with minimal
burn-in steps.

使用一种名为分期付款的神经振幅估计的无似然推断方法，为 Roman Space Space 望远镜观测到的二进制透镜微引力波事件提供实时和自动的推断。

实时无似然推断罗马二进制微引力透镜事件的摊销神经后验估计

Real-Time Likelihood-Free Inference of Roman Binary Microlensing Events  with Amortized Neural Posterior Estimation

Query optimizers rely on accurate cardinality estimates to produce good
execution plans. Despite decades of research, existing cardinality estimators
are inaccurate for complex queries, due to making lossy modeling assumptions
and not capturing inter-table correlations. In this work, we show that it is
possible to learn the correlations across all tables in a database without any
independence assumptions. We present NeuroCard, a join cardinality estimator
that builds a single neural density estimator over an entire database.
Leveraging join sampling and modern deep autoregressive models, NeuroCard makes
no inter-table or inter-column independence assumptions in its probabilistic
modeling. NeuroCard achieves orders of magnitude higher accuracy than the best
prior methods (a new state-of-the-art result of 8.5$\times$ maximum error on
JOB-light), scales to dozens of tables, while being compact in space (several
MBs) and efficient to construct or update (seconds to minutes).

本文介绍了一种名为 NeuroCard 的查询优化器，它利用了神经网络技术，结合了 join 采样和深度自回归模型，能够精确地预测复杂查询的关系及数量。该方法实现了比现有方法更高的精确度，而且具有较小的存储空间和高效的构建时间。

NeuroCard: 对所有表格的基数估计

NeuroCard: One Cardinality Estimator for All Tables

Normalizing flows (NFs) have become a prominent method for deep generative
models that allow for an analytic probability density estimation and efficient
synthesis. However, a flow-based network is considered to be inefficient in
parameter complexity because of reduced expressiveness of bijective mapping,
which renders the models unfeasibly expensive in terms of parameters. We
present an alternative parameterization scheme called NanoFlow, which uses a
single neural density estimator to model multiple transformation stages. Hence,
we propose an efficient parameter decomposition method and the concept of flow
indication embedding, which are key missing components that enable density
estimation from a single neural network. Experiments performed on audio and
image models confirm that our method provides a new parameter-efficient
solution for scalable NFs with significant sublinear parameter complexity.

本研究提出了一种名为 NanoFlow 的新参数化方案，可以使用单个神经密度估计器来模拟多个转换阶段，实现了可扩展的归一化流的新的参数有效解决方案。