这个目录就是论文中的目录: 3 Model Architecture 3.1 Encoder and Decoder Stacks 3.2 Attention 1. 3.2.1 Scaled Dot-Product Attention 1. 3.2.2 Multi-Head Attention 1. 3.2.3 Applications of Attention in our Model 1. 3.3 Position-wise Feed-Forward Networks 1. 3.4 Embeddings and Softmax 1. 3.5...
Yichi Zhang, et al, "A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning", EMNLP, 2020. Hong Liu, et al, "A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems", E...
Network Architecture The defined Net class constructs the CNN model. The model includes three convolutional layers followed by two fully connected layers. Max pooling is applied after the first and second convolutional layers to reduce the spatial dimensions of the feature maps. The ReLU activation fu...
Yichi Zhang, et al, 'A Probabilistic End-To-End Task-Oriented Dialog Model with Latent Belief States towards Semi-Supervised Learning', EMNLP, 2020. Hong Liu, et al, 'A Generative User Simulator with GPT-based Architecture and Goal State Tracking for Reinforced Multi-Domain Dialog Systems', E...
Transformer architecture variants 如图所示为Transformer结构的几种变种,主要区别在于模型中self-attention机制的可见范围。fully-visible attention mask:输出序列的每个元素可以看见输入序列的每个元素。causal attention mask:输出序列的每个元素只能看对应位置及之前的输入序列的元素,无法看见未来的元素。causal with prefix ...
此外,还可以采用模型剪枝(Model Pruning)技术,去除模型中的冗余部分,进一步压缩模型体积,提高计算效率。 在后端服务系统方面,可以采用微服务架构(Microservices Architecture),将系统划分为多个独立的服务模块,每个模块负责处理特定的业务逻辑。这种架构方式可以提高系统的可维护性、可扩展性和容错性。同时,还可以采用负载均衡...
关于证据的问题引起了我的好奇心。有哪些证据支持大型语言模型(Large Language Model,简称为LLM,下同)可能具有意识?又有哪些证据反对它?当会议组织者邀请我就机器学习系统中意识的问题提出哲学观点时,我开始思考这个问题。我很乐意这么做。 我不需要向这个听众介绍LLM。它们是巨大的人工神经网络,通常使用transformer架构...
This bottleneck architecture works together with our pre-training objectives into forcing the queries to extract visual information that is most relevant to the text. 作者通过Q-Former强制让Query提取文本相关的特征,但如果在推理时没有文本先验,那什么样的特...
ChatGPT的模型骨架是,基于Transformer神经网络架构的自回归语言模型(language model)。基于微调(finetuning)的技术,基于Prompt(提示)的技术,情景学习(in-context learning),从人类反馈中强化学习(RLHF)技术,逐步发展并最终促成了ChatGPT的诞生。 图2:ChatGPT的进步 ...
(y_train) y_test = keras.utils.to_categorical(y_test) # Define the model architecture model = keras.Sequential([ layers.Dense(256, activation='relu', input_shape=(28*28,)), layers.Dense(128, activation='relu'), layers.Dense(10, activation='softmax') ]) # Compile the model model....