mislabeling due to human error, data is labeled by multiple human labelers and the label that wins the consensus is assigned to the example. Given all the costs involved, it is imperative to utilize all the training annual meeting of the association for computational linguistics: Human language technologies. 2011. mechanism to chain multiple augmentations. It can be replaced with any other library per individual preference

wise Hierarchical clustering can be slow (has to make several merge/split decisions) No clear consensus on which of the two produces better clustering Feng Li (SDU) K-Means December 28, 2021 45 / 46

first chapter, we briefly introduced architectures like depthwise separable convolution, attention mechanism and the hashing trick. In this chapter, we will deepdive into their architectures and use them to the Hashing layer to conveniently apply the hashing trick. Figure 4-13 shows the hashing trick mechanism. On the left is the list of tokens. The tokens are hashed using the hash function in the center and how they help us outperform baseline methods. Another example in this domain is the attention mechanism, which forms the backbone of the state of the art NLP model architectures such as the Transformer

leads to unnecessary space wastage. If that is indeed the case, you might have to design your own mechanism to pack in multiple quantized values in one of the supported data types (using bit-shifting). For huffman coding and jpeg compression as examples. We talked about footprint and quality metrics as a mechanism to measure model efficiency. We learnt about quantization, a domain and model architecture agnostic

的论文《Attention Is All You Need》 中，考虑到主导序列转导模型基于编码器-解码器配置中的复杂递归或卷积 神经网络，性能最好的模型被证明还是通过注意力机制（attention mechanism）连接编码器和解码器，因而《Attention Is All You Need》 中提出了一种新的简单架构——Transformer，它完全基于注意力机制， 完全不用重复和卷积，因而这

ResNet- and ResNext- like models on all architectures due to a temporal workaround in the dispatching mechanism (commit 0494e0a) up to 21%. ‣ Known NVIDIA Ampere GPU architecture performance regressions for ResNet- and ResNext- like models on all architectures due to a temporal workaround in the dispatching mechanism (commit 0494e0a) up to 18%. ‣ Known Turing performance regressions for FastPitch and WaveGlow inference

tensor(66.) 2.1.3 广播机制 在上面的部分中，我们看到了如何在相同形状的两个张量上执行按元素操作。在某些情况下，即使形状不同， 我们仍然可以通过调用 广播机制（broadcasting mechanism）来执行按元素操作。这种机制的工作方式如 下： 1. 通过适当复制元素来扩展一个或两个数组，以便在转换之后，两个张量具有相同的形状； 2. 对生成的数组执行按元素操作。 在大多数情况下 cues） 的启发，我们将设计能够利用这些注意力提示的模型。1964年的Nadaraya‐Waston核回归（kernel regression） 正是具有 注意力机制（attention mechanism）的机器学习的简单演示。 然后继续介绍的是注意力函数，它们在深度学习的注意力模型设计中被广泛使用。具体来说，我们将展示如何 使用这些函数来设计Bahdanau注意力。Bahdanau注意力

RNN 等。我们将在第 11 章详细介绍 循环神经网络原理。 6.7.3 注意力(机制)网络 RNN 并不是自然语言处理的最终解决方案，近年来随着注意力机制(Attention Mechanism)的提出，克服了 RNN 训练不稳定、难以并行化等缺陷，在自然语言处理和图 片生成等领域中逐渐崭露头角，甚至基于自注意力 Self-attention 的一系列 Transformer 模型