论文地址:On Limitations of the Transformer Architecture 接下来,我将详细解释这篇论文的主要内容和结论。 首先,论文通过通信复杂度的方法证明了Transformer模型在函数组合问题上存在局限性。具体来说,当函数的定义域较大时,Transformer模型无法正确组合两个函数。这个结论的数学表达式为:如果函数定义域大小n满足nlogn >...
参考文献 [1] He, Ju, et al. "TransFG: A Transformer Architecture for Fine-grained Recognition." arXiv preprint arXiv:2103.07976 (2021). 想了解更多的AI技术干货,欢迎上华为云的AI专区,目前有AI编程Python等六大实战营供大家免费学习点击关注,第一时间了解华为云新鲜技术~编辑于 2021-09-11 14:36 ...
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摘要:本文解读了《TransFG: A Transformer Architecture for Fine-grained Recognition》,该论文针对细粒度分类任务,提出了对应的TransFG。 本文分享自华为云社区《论文解读系列二十:用于细粒度分类的Transformer结构—TransFG》,作者: BigDragon 。 论文地址:https://arxiv.org/abs/2103.07976 GitHub地址:https://github....
随后,Sebastian称,在论文Layer Normalization in the Transformer Architecture中,Pre-LN表现得更好,可以解决梯度问题。 这是很多或者大多数架构在实践中所采用的,但它可能导致表征崩溃。 如果层归一化在注意力和全连接层之前被放置在残差连接之中,就会实现更好的梯度。
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A transformer architecture consists of an encoder and decoder that work together. The attention mechanism lets transformers encode the meaning of words based on the estimated importance of other words or tokens. This enables transformers to process all words or tokens in parallel for faster performance...
Model Architecture(模型架构):编码器与解码器堆叠、注意力机制、位置相关的前馈神经网络、嵌入与Softmax、位置编码。 · Why Self-Attention(为什么使用自注意力机制):高效处理长序列、易于学习长期依赖关系以及提高模型的可解释性。 · Abstract(摘要) Abstract 摘要 目前主流的序列转换模型都基于复杂的循环神经网络(RNN...
The proposed architecture is adaptable to any language, but this work evaluates these models' efficiency in recognizing HWTs versus AIGTs in Arabic as an example of Semitic languages. The performance of the proposed models has been compared against the two prominent existing AI detectors, GPTZero ...
各种实验表明decoder-only模型更好,Google Brain 和 HuggingFace联合发表的 What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization? 曾经在5B的参数量级下对比了两者性能。 从技术上讲,Decoder Only的LLM始于GPT,可能最初仅仅是为了简化结构追求规模。后来发现Transformer的Atte...