We propose a general method for deep learning based point cloud analysis,
which is invariant to rotation on the inputs. Classical methods are vulnerable
to rotation, as they usually take aligned point clouds as input. Principle
Component Analysis (PCA) is a practical approach to achieve rotation
invariance. However, there are still some gaps between theory and practical
algorithms. In this work, we present a thorough study on designing rotation
invariant algorithms for point cloud analysis. We first formulate it as a
permutation invariant problem, then propose a general framework which can be
combined with any backbones. Our method is beneficial for further research such
as 3D pre-training and multi-modal learning. Experiments show that our method
has considerable or better performance compared to state-of-the-art approaches
on common benchmarks. Code is available at
this https URL

我们提出了一种基于深度学习的点云分析通用方法，对输入具有旋转不变性。通过将其作为置换不变问题进行公式化，并提出了一个通用框架，可与任何骨干网络结合使用，我们的方法在常见基准测试中相较于最先进的方法表现出显著或更好的性能，对于 3D 预训练和多模态学习等进一步研究非常有益。

旋转不变点云分析的通用框架

A general framework for rotation invariant point cloud analysis

Applications of deep learning to automatic multitrack mixing are largely
unexplored. This is partly due to the limited available data, coupled with the
fact that such data is relatively unstructured and variable. To address these
challenges, we propose a domain-inspired model with a strong inductive bias for
the mixing task. We achieve this with the application of pre-trained
sub-networks and weight sharing, as well as with a sum/difference stereo loss
function. The proposed model can be trained with a limited number of examples,
is permutation invariant with respect to the input ordering, and places no
limit on the number of input sources. Furthermore, it produces human-readable
mixing parameters, allowing users to manually adjust or refine the generated
mix. Results from a perceptual evaluation involving audio engineers indicate
that our approach generates mixes that outperform baseline approaches. To the
best of our knowledge, this work demonstrates the first approach in learning
multitrack mixing conventions from real-world data at the waveform level,
without knowledge of the underlying mixing parameters.

本文针对深度学习在多轨混音中的应用进行了研究和探索，提出了一种基于领域的模型，使用预训练的子网络和权重共享，并采用求和 / 差分立体声损失函数进行训练，得到的可用于人工调整或完善的混音参数表现出优异的表现。这是第一篇在波形级别上从实际数据中学习多轨混音约定的方法的研究。

使用可微的混音控制台进行多音轨自动混音的神经音效

Automatic multitrack mixing with a differentiable mixing console of  neural audio effects

Learning generative models for graph-structured data is challenging because
graphs are discrete, combinatorial, and the underlying data distribution is
invariant to the ordering of nodes. However, most of the existing generative
models for graphs are not invariant to the chosen ordering, which might lead to
an undesirable bias in the learned distribution. To address this difficulty, we
propose a permutation invariant approach to modeling graphs, using the recent
framework of score-based generative modeling. In particular, we design a
permutation equivariant, multi-channel graph neural network to model the
gradient of the data distribution at the input graph (a.k.a., the score
function). This permutation equivariant model of gradients implicitly defines a
permutation invariant distribution for graphs. We train this graph neural
network with score matching and sample from it with annealed Langevin dynamics.
In our experiments, we first demonstrate the capacity of this new architecture
in learning discrete graph algorithms. For graph generation, we find that our
learning approach achieves better or comparable results to existing models on
benchmark datasets.

本研究提出了一种置换不变的方法来建模图，并使用基于置换等变的多通道图神经网络来建模数据分布。 实验表明，该方法在学习离散图算法方面具有很好的能力，并在基准数据集上实现了比现有模型更好或相当的结果。