Sockeye 3 is the latest version of the Sockeye toolkit for Neural Machine
Translation (NMT). Now based on PyTorch, Sockeye 3 provides faster model
implementations and more advanced features with a further streamlined codebase.
This enables broader experimentation with faster iteration, efficient training
of stronger and faster models, and the flexibility to move new ideas quickly
from research to production. When running comparable models, Sockeye 3 is up to
126% faster than other PyTorch implementations on GPUs and up to 292% faster on
CPUs. Sockeye 3 is open source software released under the Apache 2.0 license.

Sockeye 3 是一款基于 PyTorch 的神经机器翻译工具包，实现了更快的模型实现、更高效的训练以及更快速的将新想法从研究移植到生产的灵活性，而且比其他 PyTorch 实现在 GPU 上快 126%，在 CPU 上快 292%。

Sockeye 3: 基于 PyTorch 的快速神经机器翻译

Sockeye 3: Fast Neural Machine Translation with PyTorch

We introduce modifications to state-of-the-art approaches to aggregating
local video descriptors by using attention mechanisms and function
approximations. Rather than using ensembles of existing architectures, we
provide an insight on creating new architectures. We demonstrate our solutions
in the "The 2nd YouTube-8M Video Understanding Challenge", by using frame-level
video and audio descriptors. We obtain testing accuracy similar to the state of
the art, while meeting budget constraints, and touch upon strategies to improve
the state of the art. Model implementations are available in
this https URL

本研究旨在通过引入基于注意力机制和函数逼近的方法来改进局部视频描述符集成的现有方法，并提出了创建新体系结构的见解。在使用帧级视频和音频描述符的 'The 2nd YouTube-8M Video Understanding Challenge' 中展示了我们的解决方案。通过满足预算约束条件，我们获得了类似于现有技术的测试准确性，并探讨了改进现有技术的策略。模型实现可在此 https URL 中获得。

可学习的池化方法用于视频分类

Learnable Pooling Methods for Video Classification

Verifying the correctness of Bayesian computation is challenging. This is
especially true for complex models that are common in practice, as these
require sophisticated model implementations and algorithms. In this paper we
introduce \emph{simulation-based calibration} (SBC), a general procedure for
validating inferences from Bayesian algorithms capable of generating posterior
samples. This procedure not only identifies inaccurate computation and
inconsistencies in model implementations but also provides graphical summaries
that can indicate the nature of the problems that arise. We argue that SBC is a
critical part of a robust Bayesian workflow, as well as being a useful tool for
those developing computational algorithms and statistical software.

介绍了一种名为 “基于模拟的校准（SBC）” 的通用过程，可验证使用贝叶斯算法生成后验样本的推断的正确性和一致性，并提供可指示问题性质的图形总结。