Simulation-Based Inference (SBI) is a common name for an emerging family of
approaches that infer the model parameters when the likelihood is intractable.
Existing SBI methods either approximate the likelihood, such as Approximate
Bayesian Computation (ABC) or directly model the posterior, such as Sequential
Neural Posterior Estimation (SNPE). While ABC is efficient on low-dimensional
problems, on higher-dimensional tasks, it is generally outperformed by SNPE,
which leverages function approximation. In this paper, we propose
Pseudo-Likelihood Inference (PLI), a new method that brings neural
approximation into ABC, making it competitive on challenging Bayesian system
identification tasks. By utilizing integral probability metrics, we introduce a
smooth likelihood kernel with an adaptive bandwidth that is updated based on
information-theoretic trust regions. Thanks to this formulation, our method (i)
allows for optimizing neural posteriors via gradient descent, (ii) does not
rely on summary statistics, and (iii) enables multiple observations as input.
In comparison to SNPE, it leads to improved performance when more data is
available. The effectiveness of PLI is evaluated on four classical SBI
benchmark tasks and on a highly dynamic physical system, showing particular
advantages on stochastic simulations and multi-modal posterior landscapes.

通过将神经近似引入 ABC 中，我们提出了伪似然推理 (Pseudo-Likelihood Inference, PLI) 方法，使其在具有挑战性的贝叶斯系统识别任务中具有竞争力，并且相比于 SNPE，在更多数据可用时表现出更好的性能。

伪似然推断

Pseudo-Likelihood Inference

Gradient-based optimization is now ubiquitous across graphics, but
unfortunately can not be applied to problems with undefined or zero gradients.
To circumvent this issue, the loss function can be manually replaced by a
"surrogate" that has similar minima but is differentiable. Our proposed
framework, ZeroGrads, automates this process by learning a neural approximation
of the objective function, the surrogate, which in turn can be used to
differentiate through arbitrary black-box graphics pipelines. We train the
surrogate on an actively smoothed version of the objective and encourage
locality, focusing the surrogate's capacity on what matters at the current
training episode. The fitting is performed online, alongside the parameter
optimization, and self-supervised, without pre-computed data or pre-trained
models. As sampling the objective is expensive (it requires a full rendering or
simulator run), we devise an efficient sampling scheme that allows for
tractable run-times and competitive performance at little overhead. We
demonstrate optimizing diverse non-convex, non-differentiable black-box
problems in graphics, such as visibility in rendering, discrete parameter
spaces in procedural modelling or optimal control in physics-driven animation.
In contrast to more traditional algorithms, our approach scales well to higher
dimensions, which we demonstrate on problems with up to 35k interlinked
variables.

使用神经逼近的替代函数来解决具有未定义或零梯度问题的图形优化，适用于渲染、程序建模和物理驱动动画中的非凸、非可微黑盒问题。

零梯度从不给予：学习非可导图形的局部代理损失

Zero Grads Ever Given: Learning Local Surrogate Losses for  Non-Differentiable Graphics

Neural approximations of scalar and vector fields, such as signed distance
functions and radiance fields, have emerged as accurate, high-quality
representations. State-of-the-art results are obtained by conditioning a neural
approximation with a lookup from trainable feature grids that take on part of
the learning task and allow for smaller, more efficient neural networks.
Unfortunately, these feature grids usually come at the cost of significantly
increased memory consumption compared to stand-alone neural network models. We
present a dictionary method for compressing such feature grids, reducing their
memory consumption by up to 100x and permitting a multiresolution
representation which can be useful for out-of-core streaming. We formulate the
dictionary optimization as a vector-quantized auto-decoder problem which lets
us learn end-to-end discrete neural representations in a space where no direct
supervision is available and with dynamic topology and structure. Our source
code will be available at this https URL

本研究提出了一种使用字典方法进行特征网格压缩的方法，可以将其内存消耗降低 100 倍，同时实现了多分辨率表示，其优化方法可以作为离散神经表示的端到端训练解决方案，适用于无法进行直接监督并具有动态拓扑和结构的空间。