(1) The enhanced capability of Graph Neural Networks (GNNs) in unsupervised
community detection of clustered nodes is attributed to their capacity to
encode both the connectivity and feature information spaces of graphs. The
identification of latent communities holds practical significance in various
domains, from social networks to genomics. Current real-world performance
benchmarks are perplexing due to the multitude of decisions influencing GNN
evaluations for this task. (2) Three metrics are compared to assess the
consistency of algorithm rankings in the presence of randomness. The
consistency and quality of performance between the results under a
hyperparameter optimisation with the default hyperparameters is evaluated. (3)
The results compare hyperparameter optimisation with default hyperparameters,
revealing a significant performance loss when neglecting hyperparameter
investigation. A comparison of metrics indicates that ties in ranks can
substantially alter the quantification of randomness. (4) Ensuring adherence to
the same evaluation criteria may result in notable differences in the reported
performance of methods for this task. The $W$ Randomness coefficient, based on
the Wasserstein distance, is identified as providing the most robust assessment
of randomness.

Graph Neural Networks 在无监督社区检测中的增强能力归因于它们能够编码图的连通性和特征信息空间，该研究讨论了性能基准、超参数优化和随机性对结果的一致性和质量的影响。

GNN 社群检测中评估不确定性：随机性量化措施比较

Uncertainty in GNN Learning Evaluations: A Comparison Between Measures  for Quantifying Randomness in GNN Community Detection

In recent years, the field of neural machine translation (NMT) for SPARQL
query generation has witnessed a significant growth. Recently, the
incorporation of the copy mechanism with traditional encoder-decoder
architectures and the use of pre-trained encoder-decoders have set new
performance benchmarks. This paper presents a large variety of experiments that
replicate and expand upon recent NMT-based SPARQL generation studies, comparing
pre-trained and non-pre-trained models, question annotation formats, and the
use of a copy mechanism for non-pre-trained and pre-trained models. Our results
show that either adding the copy mechanism or using a question annotation
improves performances for nonpre-trained models and for pre-trained models,
setting new baselines for three popular datasets.

本文介绍了关于 NMT 的 SPARQL 查询语句生成的最新研究，并比较了预训练和非预训练模型、问题注释格式以及无需预训练和预训练模型的复制机制的使用。实验结果表明，无论是添加复制机制还是使用问题注释都可以提高非预训练模型和预训练模型的性能，为三个流行数据集设定新的基准线。

自然语言到 SPARQL 查询生成的抄袭机制的全面评估

A Comprehensive Evaluation of the Copy Mechanism for Natural Language to  SPARQL Query Generation

Machine learning (ML) needs industry-standard performance benchmarks to
support design and competitive evaluation of the many emerging software and
hardware solutions for ML. But ML training presents three unique benchmarking
challenges absent from other domains: optimizations that improve training
throughput can increase the time to solution, training is stochastic and time
to solution exhibits high variance, and software and hardware systems are so
diverse that fair benchmarking with the same binary, code, and even
hyperparameters is difficult. We therefore present MLPerf, an ML benchmark that
overcomes these challenges. Our analysis quantitatively evaluates MLPerf's
efficacy at driving performance and scalability improvements across two rounds
of results from multiple vendors.

MLPerf 是一个机器学习基准测试，用于量化评估训练性能和可扩展性，解决了优化训练、随机性和系统差异等挑战。

MLPerf 训练基准测试

MLPerf Training Benchmark

In this work, we model abstractive text summarization using Attentional
Encoder-Decoder Recurrent Neural Networks, and show that they achieve
state-of-the-art performance on two different corpora. We propose several novel
models that address critical problems in summarization that are not adequately
modeled by the basic architecture, such as modeling key-words, capturing the
hierarchy of sentence-to-word structure, and emitting words that are rare or
unseen at training time. Our work shows that many of our proposed models
contribute to further improvement in performance. We also propose a new dataset
consisting of multi-sentence summaries, and establish performance benchmarks
for further research.

该研究使用注意力编码 - 解码循环神经网络模型抽象化文本摘要，并在两个不同的语料库上展示其实现最先进的性能。同时，该研究还提出了几种解决文摘中的关键问题的新模型，比如建模关键词、捕捉句子到单词结构的层次以及提取训练时很少出现的单词等。此外，研究还建立了一个多句子的文摘数据集，以便后续的新研究能建立性能基准。