几篇论文实现代码:《Sparse Sequence-to-Sequence Models》(ACL 2019) GitHub: http://t.cn/AiQID5Y1 《RANet: Ranking Attention Network for Fast Video Object Segmentation》(ICCV 2019) GitHub: http://t...
Sparse Softmax的思想源于《From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification》、《Sparse Sequence-to-Sequence Models》等文章。里边作者提出了将Softmax稀疏化的做法来增强其解释性乃至提升效果 不够稀疏的Softmax
Sparse Sequence-to-Sequence Models @inproceedings{entmax, author = {Peters, Ben and Niculae, Vlad and Martins, Andr{\'e} FT}, title = {Sparse Sequence-to-Sequence Models}, booktitle = {Proc. ACL}, year = {2019}, url = {https://www.aclweb.org/anthology/P19-1146} } ...
Sparse Softmax 的思想源于《From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification》、《Sparse Sequence-to-Sequence Models》等文章。里边作者提出了将 Softmax 稀疏化的做法来增强其解释性乃至提升效果 不够稀疏的 Softmax ...
Ben Peters, Vlad Niculae, and André FT Martins.Sparse sequence-to-sequence models. arXiv preprint arXiv:1905.05702, 2019. 作者给出了这两个参考文献。。。 为了更好地进行训练,作者引入了对抗损失函数,将 transformer 模型嵌入到 GAN 的框架中,进行对抗训练。整体算法框架图如下所示: ...
Accurate modeling of DNA sequences requires capturing distant semantic relationships between the nucleotide acid bases. Most existing deep neural network models face two challenges: (1) they are limited to short DNA fragments and cannot capture long-range interactions, and (2) they require many ...
B. 另一种解决方案是采用 SOLOFusion、StreamPETR 等方法中使用的 sequence 训练方案,省显存省时间,我们未来可能会尝试。 结论 本文中,我们提出了一种全稀疏的单阶段 3D 目标检测器 SparseBEV。SparseBEV 通过尺度自适应自注意力、自适应时...
另一种解决方案是采用 SOLOFusion、StreamPETR 等方法中使用的 sequence 训练方案,省显存省时间,我们未来可能会尝试。 5. 结论 本文中,我们提出了一种全稀疏的单阶段 3D 目标检测器 SparseBEV。SparseBEV 通过尺度自适应自注意力、自适应时空采样、自适应融合三个核心模块提升了基于稀疏 query 模型的自适应性,取得...
目前有两种解决方案:A. 将部分视频帧的梯度截断。我们开源的 config 中有个 stop_prev_grad 选项,它会将所有之前帧都以 no_grad 模式推理,只有当前帧会有梯度回传。B. 另一种解决方案是采用 SOLOFusion、StreamPETR 等方法中使用的 sequence 训练方案,省显存省时间,我们未来可能会尝试。
Transformers are powerful sequence models, but require time and memory that grows quadratically with the sequence length. In this paper we introduce sparse factorizations of the attention matrix which reduce this to O(nn). We also introduce a) a variation on architecture and initialization to ...