(1)通过Self-Attention(如图1左所示),每个词都和所有词计算Attention,因此不论序列中词与词之间的距离有多长,他们之间的最大路径长度都为1,因此可以捕获更长的依赖关系。 (2)提出Multi-head Attention(MHA)(如图1右所示), 通过多个Head学习不同的子空间语义,最后通过Concat和Linear操作降维至单Head的Size,这相当...
(1)通过Self-Attention(如图1左所示),每个词都和所有词计算Attention,因此不论序列中词与词之间的距离有多长,他们之间的最大路径长度都为1,因此可以捕获更长的依赖关系。 (2)提出Multi-head Attention(MHA)(如图1右所示), 通过多个Head学习不同的子空间语义,最后通过Concat和Linear操作降维至单Head的Size,这相当...
(1) Ps and Qs: Quantization-Aware Pruning for Efficient Low Latency Neural ...Ps and Qs: Quan...
ERNIE(Enhanced Representation through kNowledge IntEgration): ERNIE是由百度提出的一系列BERT模型,针对不同任务和不同语言需求进行优化和扩展。 MT-DNN(Multi-Task Deep Neural Network): MT-DNN是由微软提出的一种多任务深度神经网络,基于BERT模型,通过共享层来处理多个下游任务。 Unified Language Model (UniLM): ...
// Create a neural network object ncnn::Net ssdlite; // Load the network parameters and weights from a trained SSD-Lite model ssdlite.load_param("ssdlite.param"); ssdlite.load_model("ssdlite.bin"); // Define the input data for the network ...
在深度学习的初期，最著名的语言模型是RNN，Recurrent Neural Network，中文叫循环神经网络。RNN 模型与...
1.Model size: GPT-3 has a massive number of parameters (over 175 billion) and has been trained on a diverse range of texts, which has enabled it to develop a deep understanding of language. The large size of the model allows it to generate coherent and meaningful responses to a wide ra...
The filters are designed to be small and local, allowing them to capture the local relationships in the data. The pooling layer reduces the spatial size of the feature map and helps to reduce the computational cost and overfitting. The activation function introduces non-linearity into the network...
(2)提出Multi-head Attention(MHA)(如图1右所示), 通过多个Head学习不同的子空间语义,最后通过Concat和Linear操作降维至单Head的Size,这相当于多个子空间语义表示的Ensemble。 [图二 Transformer 整体结构] (3)整体结构遵从Encoder-Decoder的形式,其中Decoder的每个...
(size) # 定义一个层归一化(Layer Normalization)操作,使用size作为输入维度 self.dropout = nn.Dropout(dropout) # 定义一个dropout层 # 定义前向传播函数,输入参数x是输入张量,sublayer是待执行的子层操作 def forward(self, x, sublayer): # 将残差连接应用于任何具有相同大小的子层 # 首先对输入x进行层...