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
Modern vector embedding models like BERT condensed meaning into hundreds of numerical dimensions accurately estimating semantic similarity. However, transformer architectures with self-attention don’t scale beyond 512–1024 tokens due to exploding computation. Without the capacity ...
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 b...
to the propagation of the frame. It is based on the pre-calculated segmentation mask of a single frame, so to obtain the final segmentation mask, a multi-step Refinement is required. The essence of this method is still the extraction of a single frame plus the propagation between frames, a...
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....
Kim Y (2014) Convolutional neural networks for sentence classification. In: Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). Association for Computational Linguistics, Doha, Qatar, pp 1746–1751. https://doi.org/10.3115/v1/D14-1181. https://aclanth...
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...
Our approach is also related to the work by Harley et al. on learning dense convolutional embeddings[24], which trains a deep network to produce pixel-wise embeddings for the task of semantic segmentation. Our work differs from theirs in that our network produces not only pixel-wise embeddings...
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...
Their approach involved unsupervised training for the 3D convolutional decoder and used it to guide the training of the 3D-CAE. For the hyperspectral image classification method used for crop classification, the following questions arise: How should the model based on semantic segmentation consider the...