在深度学习算法中,参数共享(Parameter Sharing)是一种重要的技术,它通过共享模型的参数来减少模型的复杂度,并提升模型的性能和泛化能力。本文将介绍参数共享的概念、原理以及在深度学习算法中的应用。 参数共享的概念 参数共享指的是在模型的不同部分使用相同的参数。在传统的机器学习算法中,每个特征都有自己独立的参数...
学生 本文提出一个使用预训练模型进行多任务学习的新方法,Task Adaptive Parameter Sharing (TAPS),自适应的选择已有层的最小子集,重新训练选中的这些层。 (用来选择层的方法有很多,大多比较难训练,比如强化学习,或者加一个模块选择层;本文的创新点在于将层的选择简化为一个可持续的问题,使用stochastic gradient descent...
"Cooperative multi-agent control using deep reinforcement learning." International Conference on Autonomous Agents and Multiagent Systems. Springer, Cham, 2017. 问题背景 本文研究部分观测且无通信条件下的多智能体协作问题,核心思想是学习一个全局共享的网络,在执行的时候由于每个agent看到不同观测,因此会产生不...
Deep learning approaches that have produced breakthrough predictive models in computer vision, speech recognition and machine translation are now being successfully applied to problems in regulatory genomics. However, deep learning architectures used thus far in genomics are often directly ported from comput...
Collaborative operation optimization of distribution system and virtual power plants using multi‐agent deep reinforcement learning with parameter‐sharing... To enhance computational efficiency and privacy, the paper presents the parameter‐sharing twin delayed deep deterministic policy gradient approach. This...
Re-training and parameter sharing with the Hash trick for compressing convolutional neural networksModel compressionMachine learningParameter learning and tuningComputational and artificial intelligenceAs an ubiquitous technology for improving machine intelligence, deep learning has largely taken the dominant ...
《Efficient Neural Architecture Search via Parameter Sharing》 Hieu Pham · Melody Y. Guan · Barret Zoph · Quoc V. Le · Jeff Dean 2018-02-12 ICML 2021有关NAS的论文 在paperdigest网站上显示,这篇文章的影响因子高达9! 必看论文! 注意到这篇论文正是Zoph大神写的。
The parameters of the original network remain fixed, yielding a high degree of parameter sharing. To demonstrate adapter's effectiveness, we transfer the recently proposed BERT Transformer model to 26 diverse text classification tasks, including the GLUE benchmark. Adapters attain near state-of-the-...
On these function classes, deep learning also attains the minimax rate up to log factors of the sample size, and linear methods are still suboptimal if the assumed sparsity is strong. We also point out that the parameter sharing of deep neural networks can remarkably reduce the complexity of ...
Parameter Sharing对于卷积运算来讲也是至关重要,因为如何sharing直接决定了参数的数量。在GCN可能更尤为重要了,因为graph上每个顶点的度都不一样,所以不能按照CNN的方式来进行sharing。 这里介绍三种目前较为流行的GCN模型。 1 Defferrard, M., Bresson, X., & Vandergheynst, P. (2016) ...