本文通过将多元TSF转换为一个新的时空序列公式来解决这个问题,其中每个输入标记代表给定时间步长的单个变量的值。然后,Long-Range Transformers可以学习空间、时间和价值信息之间的交互作用,共同沿着这个扩展序列。我们的方法,我们称之为Spacetimeformer,适用于由依赖于预定义的可变图的图神经网络控制的高维预测问题。我们在...
通过进一步微调这些预训练模型,我们可以有效地转移这些知识以有益于下游任务。 现有的 long-range transformers 研究通常需要从头开始对所提出的模型进行预训练,以适应新的架构和长输入。 然而,巨大的训练开销为这些方法在不同语言模型中的广泛使用设置了障碍。受此启发,我们探索利用现有预训练模型并通过持续训练使其适应...
有一些架构(比如Longformer-Encoder-Decoder (LED; Beltagy et al., 2020))可以用先前预训练的模型,但仍需要进一步训练位置嵌入或者全局注意力权重,这些计算也都是昂贵的。 作者提出了Unlimiformer,这是一种基于检索的方法,用于增强预训练过的语言模型,使得能够在测试时接受无限长度的输入。 模型 Encoding 使用给定模...
Many natural language processing tasks benefit from long inputs, but processing long documents with Transformers is expensive -- not only due to quadratic attention complexity but also from applying feedforward and projection layers to every token. However, not all tokens are equally important, ...
decision-transformers.md deep-learning-with-proteins.md deep-rl-a2c.md deep-rl-dqn.md deep-rl-intro.md deep-rl-pg.md deep-rl-ppo.md deep-rl-q-part1.md deep-rl-q-part2.md deploy-hugging-face-models-easily-with-amazon-sagemaker.md deploy-tfserving-kubernetes.md deploy-vertex...
To address this problem, we propose a Long-range Graph Transformer for early rumor detection (LGT), which uses transformers to capture long-range dependencies between users. First, we use a graph convolutional attentive network to extract the publishing features. Second, we combine graph neural ...
14_long_range_transformers CompressiveTransformer.png EfficientTransformerTaxonomy.png Linformer.png Longformer.png Performer.png 150_autoformer 151_mms 151_policy_ntia_rfc 152_ethics_soc_4 153_text_to_webapp 155_inference_endpoints_llm 156_ai_webtv 156_huggylingo 157_dpo_trl 158_...
[论文简析]Location-Aware Self-Supervised Transformers for Semantic Seg.[2212.02400] 2241 2 9:18 App [论文简析]Object-Centric Learning with Slot Attention[2006.15055] 1989 3 10:24 App [论文速览]Invariant Information Clustering for Unsupervised Image...[1807.06653] 3320 1 9:41 App [论文简析]Crossw...
【ARXIV2203】Efficient Long-Range Attention Network for Image Super-resolution 代码:https://github.com/xindongzhang/ELAN 1、研究动机 尽管Transformer已经“主宰”了CV领域,在
5.Were there no transformers to adjust the voltage, long-distance transmission of electricity would be impossible.若无变压器来调节电压,远距离输送电力是不可能的。 6.Lang-range Transport of Asian Dust and Its Effects on Ocean Ecosystem亚洲沙尘的远距离输送及对海洋生态系统的影响 7.APPLICATION OF PULSE...