ConvE uses 2D convolution over embeddings and multiple layers of nonlinear features to model knowledge graphs. The model can be efficiently trained and scalable to large knowledge graphs. However, there is no structure enforcement in the embedding space of ConvE. The recent graph convolutional network...
Convolutional Neural Networks (CNNs) with Bilinear Pooling, initially in their full form and later using compact representations, have yielded impressive p... C Yin,Z Feng,W Jiang,... - IEEE Conference on Computer Vision & Pattern Recognition 被引量: 33发表: 2017年 Designing Reliable Server ...
It is anticipated that BERT will provide rich contextual embeddings, allowing for a comprehensive understanding of semantic relationships within sentences and questions. Specifically, we propose an incorporation of BERT, a state-of-the-art pre-trained language model with the Convolutional End-to-End ...
Such a density matrix, together with a joint representation of the question and the answer, can be integrated into neural network architectures (e.g., 2-dimensionai convolutional neural networks). Experiments on the TREC-QA and WIKIQA datasets have verified the effectiveness of our proposed models...
In this paper, a framework CorefRoCNN based on RoBERTa and convolutional neural network (CNN) for end-to-end coreference resolution of geological entities... B Wan,S Dong,D Chu,... - 《Information Processing & Management Libraries & Information Retrieval Systems & Communication Networks An Inter...
Anomaly Detection in Scientific Workflows using End-to-End Execution Gantt Charts and Convolutional Neural Networks Fundamental progress towards reliable modern science depends on accurate anomaly detection during application execution. In this paper, we suggest a novel approach to tackle this problem by ...
Schlichtkrull M, Kipf TN, Bloem P, Van Den Berg R, Titov I, Welling M (2018) Modeling relational data with graph convolutional networks. In: Proceedings of European semantic web conference. Springer, pp 593–607. https://doi.org/10.1007/978-3-319-93417-4_38 Yang B, Yih SWt, He X...
We investigate four pretrained end-to-end architectures: twoConvolutional Neural Networks(CNN) architectures trained for the tasks of(i) speaker recognitionand(ii)dialect identification, as well as two Transformer architectures trained to(iii) reconstruct the masked signal.3We chose these architectures ...
and then feed them into convolutional neural networks. Despite of the gains in performance, such operations need plenty of extra com- puting resources. In this context, we alter to adopt bidirectional GRU [34] to model the motion and capture the temporal infor- mation between continuous frames....
FLGCNN: A novel fully convolutional neural network for end-to-end monaural speech enhancement with utterance-based objective functions[J]. Applied Acoustics, 2020, 170: 107511. 摘要 提出了一种新的全卷积神经网络(FCN),称为FLGCNN,用于解决时域端到端语音增强问题。提出的FLGCNN主要建立在编码器和译码...