To compete with existing mobile architectures, MobileViG introduces Sparse
Vision Graph Attention (SVGA), a fast token-mixing operator based on the
principles of GNNs. However, MobileViG scales poorly with model size, falling
at most 1% behind models with similar latency. This paper introduces Mobile
Graph Convolution (MGC), a new vision graph neural network (ViG) module that
solves this scaling problem. Our proposed mobile vision architecture,
MobileViGv2, uses MGC to demonstrate the effectiveness of our approach. MGC
improves on SVGA by increasing graph sparsity and introducing conditional
positional encodings to the graph operation. Our smallest model,
MobileViGv2-Ti, achieves a 77.7% top-1 accuracy on ImageNet-1K, 2% higher than
MobileViG-Ti, with 0.9 ms inference latency on the iPhone 13 Mini NPU. Our
largest model, MobileViGv2-B, achieves an 83.4% top-1 accuracy, 0.8% higher
than MobileViG-B, with 2.7 ms inference latency. Besides image classification,
we show that MobileViGv2 generalizes well to other tasks. For object detection
and instance segmentation on MS COCO 2017, MobileViGv2-M outperforms
MobileViG-M by 1.2 $AP^{box}$ and 0.7 $AP^{mask}$, and MobileViGv2-B
outperforms MobileViG-B by 1.0 $AP^{box}$ and 0.7 $AP^{mask}$. For semantic
segmentation on ADE20K, MobileViGv2-M achieves 42.9% $mIoU$ and MobileViGv2-B
achieves 44.3% $mIoU$. Our code can be found at
https://github.com/SLDGroup/MobileViGv2.

MobileViGv2 使用 Mobile Graph Convolution (MGC) 模块来解决模型尺寸扩展问题，通过增加图稀疏性和引入条件位置编码到图操作中，提高了在图像分类等任务中的准确性。

为移动视觉扩展图卷积

Scaling Graph Convolutions for Mobile Vision

Prior work on multilingual sentence embedding has demonstrated that the
efficient use of natural language inference (NLI) data to build
high-performance models can outperform conventional methods. However, the
potential benefits from the recent ``exponential'' growth of language models
with billions of parameters have not yet been fully explored. In this paper, we
introduce Multilingual Sentence T5 (m-ST5), as a larger model of NLI-based
multilingual sentence embedding, by extending Sentence T5, an existing
monolingual model. By employing the low-rank adaptation (LoRA) technique, we
have achieved a successful scaling of the model's size to 5.7 billion
parameters. We conducted experiments to evaluate the performance of sentence
embedding and verified that the method outperforms the NLI-based prior
approach. Furthermore, we also have confirmed a positive correlation between
the size of the model and its performance. It was particularly noteworthy that
languages with fewer resources or those with less linguistic similarity to
English benefited more from the parameter increase. Our model is available at
this https URL

我们介绍了基于 NLI 的多语言句子嵌入模型 m-ST5，通过扩展现有的单语模型 Sentence T5 以低秩适应（LoRA）技术成功将模型参数规模扩展到 57 亿，并通过实验证实方法优于基于 NLI 的先前方法，尤其是对资源较少或与英语相似性较低的语言受益更多。

多语句 - T5：可扩展的多语句编码器适用于多语言应用

Multilingual Sentence-T5: Scalable Sentence Encoders for Multilingual  Applications

Time series forecasting is one of the most essential and ubiquitous tasks in
many business problems, including demand forecasting and logistics
optimization. Traditional time series forecasting methods, however, have
resulted in small models with limited expressive power because they have
difficulty in scaling their model size up while maintaining high accuracy. In
this paper, we propose Forecasting orchestra (Forchestra), a simple but
powerful framework capable of accurately predicting future demand for a diverse
range of items. We empirically demonstrate that the model size is scalable to
up to 0.8 billion parameters. The proposed method not only outperforms existing
forecasting models with a significant margin, but it could generalize well to
unseen data points when evaluated in a zero-shot fashion on downstream
datasets. Last but not least, we present extensive qualitative and quantitative
studies to analyze how the proposed model outperforms baseline models and
differs from conventional approaches. The original paper was presented as a
full paper at ICDM 2022 and is available at:
this https URL

提出了一种名为 Forecasting orchestra（Forchestra）的时间序列预测框架，具有可扩展的模型规模和预测精度，能够在需求预测和物流优化等业务问题中准确预测未来需求。