The expression "MetaLearning" is tossed around in Deep Learning writing often referencing "AutoML", "Few-Shot Learning", or "Neural Architecture Search" when in reference to the robotized design of neural system architectures. Rising up out of entertainingly titled papers such as "Figuring out ...
图8 meta learning和机器学习的损失函数对比—引自参考1 注意meta learning虽然是“learn to learn”,但是在实际使用时仍然需要调参数,例如解优化问题\phi^*=argminL\left( \phi \right),找到一个好的learning algorithm,找到之后,这个learning algorithm可以用在一个新的任务上(此时不需要调参数,已经学到了最优...
所以对这一类学习算法的特性有一个比较恰当的描述:Learning how to learn. Meta learning 作为一个思维框架,有很多不同的应用版本。我们首先用一个具体的例子解释这个过程,再由这个例子总结对框架一般性的思考。 顾名思义,Model-agnostic (模型无关) meta learning 将模型作为一个缺省的部分,也就是说可以代入任何...
Learning to Discretize: Solving 1D Scalar Conservation Laws via Deep Reinforcement Learning, (2019), Yufei Wang, Ziju Shen, Zichao Long, Bin Dong. 书籍 Hands-On Meta Learning with Python: Meta learning using one-shot learning, MAML, Reptile, and Meta-SGD with TensorFlow, (2019), Sudharsan R...
we need our agents to learn how to learn new tasks faster by reusing previous experience, rather than considering each new task in isolation. This approach of learning to learn, or meta-learning, is a key stepping stone towards versatile agents that can continually learn a wide variety of tas...
Meta learning, also called “learning to learn,” is a subcategory of machine learning that trains artificial intelligence (AI) models to understand and adapt to new tasks on their own.
Deep-learning professionals do not need to learn a new framework to start using MetaTF immediately. In three steps, MetaTF users can go from designing and training CNNs to converting them for deployment on the Akida neural processor to fully leverage neuromorphic computing and overcome the challen...
论文阅读笔记《Deep Meta-Learning: Learning to Learn in the Concept Space》,程序员大本营,技术文章内容聚合第一站。
meta-learning; few-shot learning; meta-reinforcement learning Graphical Abstract1. Introduction Meta-learning aims to rapidly learn new tasks by leveraging prior knowledge from related tasks [1,2]. A popular optimization-based meta-learning method is Model-Agnostic Meta-Learning (MAML) [3], which...
in machine learning refers to learning algorithms that learn from other learning algorithms. Most commonly, this means the use of machine learning algorithms that learn how to best combine the predictions from other machine learning algorithms in the field of ensemble learning.Nevertheless...