In this paper, the problem of semantic information extraction for resource
constrained text data transmission is studied. In the considered model, a
sequence of text data need to be transmitted within a communication
resource-constrained network, which only allows limited data transmission.
Thus, at the transmitter, the original text data is extracted with natural
language processing techniques. Then, the extracted semantic information is
captured in a knowledge graph. An additional probability dimension is
introduced in this graph to capture the importance of each information. This
semantic information extraction problem is posed as an optimization framework
whose goal is to extract most important semantic information for transmission.
To find an optimal solution for this problem, a Floyd's algorithm based
solution coupled with an efficient sorting mechanism is proposed. Numerical
results testify the effectiveness of the proposed algorithm with regards to two
novel performance metrics including semantic uncertainty and semantic
similarity.

本文研究了资源受限文本数据传输的语义信息提取问题。在所考虑的模型中，需要在通信资源受限的网络中传输文本数据序列，该网络仅允许有限的数据传输。因此，在发送端，使用自然语言处理技术提取原始文本数据，然后将提取的语义信息捕捉到知识图中。该图引入了一个额外的概率维度，以捕捉每个信息的重要性。这个语义信息提取问题被提出为一个优化框架，其目标是提取最重要的语义信息进行传输。为了找到这个问题的最优解，提出了一种基于 Floyd 算法的解决方案，结合了高效的排序机制。数值结果证明了所提算法在语义不确定性和语义相似性等两个新颖性能度量方面的有效性。

基于概率图的文本语义信息提取

Semantic Information Extraction for Text Data with Probability Graph

Porting state of the art deep learning algorithms to resource constrained
compute platforms (e.g. VR, AR, wearables) is extremely challenging. We propose
a fast, compact, and accurate model for convolutional neural networks that
enables efficient learning and inference. We introduce LCNN, a lookup-based
convolutional neural network that encodes convolutions by few lookups to a
dictionary that is trained to cover the space of weights in CNNs. Training LCNN
involves jointly learning a dictionary and a small set of linear combinations.
The size of the dictionary naturally traces a spectrum of trade-offs between
efficiency and accuracy. Our experimental results on ImageNet challenge show
that LCNN can offer 3.2x speedup while achieving 55.1% top-1 accuracy using
AlexNet architecture. Our fastest LCNN offers 37.6x speed up over AlexNet while
maintaining 44.3% top-1 accuracy. LCNN not only offers dramatic speed ups at
inference, but it also enables efficient training. In this paper, we show the
benefits of LCNN in few-shot learning and few-iteration learning, two crucial
aspects of on-device training of deep learning models.

本研究提出了一种快速、紧凑且准确的卷积神经网络模型 LCNN，能够在资源受限的平台上实现高效的学习和推断，在 ImageNet 数据集上取得了较好的速度和精度表现，并展示了其在深度学习模型的设备端训练中的优势。