In this work we propose the Transformer, a model architecture eschewing recurrence and instead relying entirely on an attention mechanism to draw global dependencies between input and output. The Transformer allows for significantly more parallelization and can reach a new state of the art in translati...
The Transformer在一些特殊任务上超越了Google Neural Machine Translation model(RNN+Attention)。The Transformer最大的优势来源于它的并行化计算。实际上,Google Cloud建议使用Transformer作为参考模型来使用其Cloud TPU产品。所以,让我们来分解这个模型,看看它是如何工作的。 A High-Level Look(一个宏观的概括) 首先,我...
To address this, the transformer adds a vector to each input embedding. These vectors follow a specific pattern that the model learns, which helps it determine the position of each word, or the distance between different words in the sequence. The intuition here is that adding these values to...
1.Transformer 架构 先放一张网上已经包浆的图(好用好懂才会包浆): Transformer最常见的架构图 接下来我们从下往上,一点点看看图片中每一个元素的含意与作用 Input(prompt): 作为Transformer的输入,这里的prompt 一般是分词之后的内容 Input Embedding: Transformer无法理解文本,他只做矩阵计算,所以,这里必须要有这一...
RT-1 introduces a language-conditioned multitask imitation learning policy on over 500 manipulation tasks. First effort at Google DeepMind to make some drastic changes such as: bet on action tokenization, Transformer architecture, switch from RL to BC. Culmination of 1.5 years of demonstration data...
讲者: 张景昭清华大学交叉信息研究院助理教授报告题目:On the (Non)smoothness of Neural Network Training报告摘要: In this talk, we will discuss the following question―why is neural network training non-smooth from an optimization p, 视频播放量 793、弹幕
Transformer是只基于自注意力机制的序列到序列架构。因为并行计算能力以及高性能。使得它在NLP领域中大受欢迎。 现在常见的几个深度学习框架都实现了transformer,这让很多学生都能够方便使用到transformer。但是这也存在一个弊端,他会让我们忽略模型的一些细节。
Model efficiency, data efficiency and learning paradigm efficiency will be discussed respectively. As some highlights, I will introduce our recent progress on model compression via tensor representation, data efficiency through the lens of generalization analysis and a decentralized federated learning ...
Initially, transformer architecture didn’t grab much attention outside the machine learning community. But shortly after that, researchers at Google trained a new transformer model for NLP tasks that broke records on several fronts. The model was trained to meet two objectives: ...