所以attention到底是什么? 13:57 自注意力(self-attention)详解 12:45 positional encoding详解(1) 10:57 positional encoding详解(2) 12:49 多头注意力Multi-headed attention的原理是什么 04:46 为何Transformer需要残差连接(Residual Connection)? 05:36 Transformer中的层归一化(Layer Normalization) 07:...
Hence, we introduce Gated Attention Coding (GAC), a plug-and-play module that leverages the multi-dimensional gated attention unit to efficiently encode inputs into powerful representations before feeding them into the SNN architecture. GAC functions as a preprocessing layer that does not disrupt ...
Noreen Zaffer put forward a CNN-LSTM multi-step prediction model that incorporated feature data with an attention mechanism, showcasing an impressive accuracy rate of nearly 99%, with effective application across varying conditions such as peak and non-peak hours, and differentiating between working ...
Support PyTorch INT8 inference. Provide PyTorch INT8 quantiztion tools. Integrate the fused multi-head attention kernel of TensorRT into FasterTransformer. Add unit test of SQuAD. Update the missed NGC checkpoints. Sep 2020 Support GPT2 Release the FasterTransformer 3.0 Support INT8 quantization ...
Compared to AdaShift-MA, (1) AdaShift-MA-N1 jointly attends to the main and the residual features through a united attention process. That is, for a layer that converges the main and the residual features, AdaShift-MA-N1 produces two patches of ...
Noreen Zaffer put forward a CNN-LSTM multi-step prediction model that incorporated feature data with an attention mechanism, showcasing an impressive accu- racy rate of nearly 99%, with effective application across varying conditions such as peak and non-peak hours, and differentiating between ...
Pytorch implmentation of Paragraph-level Neural Question Generation with Maxout Pointer and Gated Self-attention Networks - wly-thu/neural-question-generation
Official Pytorch Code for "Medical Transformer: Gated Axial-Attention for Medical Image Segmentation" - MICCAI 2021 - jeya-maria-jose/Medical-Transformer
All experiments were conducted in a Pytorch environment and with an NVIDIA Tesla V100 GPU, using ResNet-50-FPN as the backbone of all networks in the experiments. For training, we used an SGD optimizer with an initial learning rate of 0.0025, a batch size of 4, a total of 36 epochs, ...
The attention vector is then separated in the channel dimension to obtain the weights 𝑤1w1 and 𝑤2w2 for 𝑓1f1 and 𝑓2f2, respectively. Finally, 𝑓1f1 and 𝑓2f2 are weighted according to these weights and added to obtain the output of the SK Fusion layer. The process is as...