Continuous Meta-Learning without TasksJames HarrisonApoorva SharmaChelsea FinnMarco PavoneNeural Information Processing Systems
meta learning很有效是已经钦定了吗
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments 核心问题 研究问题:如何在nonstationary的环境中,快速的学习出相应的策略(这里的Competitive是导致Competitive的一种原因) 假设条件:nonstationary的环境是由多个stationary的task切换引起的,任务切换具有Markov chain的性质 主要想法:找到一...
A Continuous Performance Task (CPT) is a type of task where individuals engage in a constant-difficulty activity for minutes or tens of minutes without interruptions. These tasks are commonly used to assess attention and impulsivity, and are often employed in diagnosing conditions like ADHD in neur...
Reinforcement learning (RL) approaches that combine a tree search with deep learning have found remarkable success in searching exorbitantly large, albeit discrete action spaces, as in chess, Shogi and Go. Many real-world materials discovery and design applications, however, involve multi-dimensional ...
17 recently used a meta-learning approach to perform state-of-the-art TCR-pMHC binding prediction for OOD peptides. Due to the relatively small volume of TCR-pMHC data available, such strategies are still not expected to perform with complete accuracy in the full state-space of TCR-pMHC ...
cST-ML tackles the traffic dynamics prediction challenges by advancing the Bayesian black-box meta-learning framework through the following new points: 1) cST-ML captures the dynamics of traffic prediction tasks using variational inference; 2) cST-ML has novel designs in architecture, where CNN and...
因此,我们在之前基于梯度的模型不可知元学习(MAML,model-agnostic meta-learning)工作的基础上构建了我们的方法,该工作已经在少数设置下成功使用。在本节中,我们从概率的角度重新推导了用于多任务强化学习的MAML,然后将其扩展到动态变化的任务。 3.1 模型不可知元学习的概率理论(MAML)...
We, humans, are great at problem-solving and innovating, and the result is Artificial Intelligence and deep learning models that answer all the questions. Through this, the machine can learn every new addition without human intervention and, in the meantime, let the team direct new inquiries to...
他们会找一个和新任务最相似的学习过的任务,将对应的task embedding用来初始化新任务的e,并且再进行训练微调,来找到最适合于新任务的task embedding,这也就实现了跨任务泛化(这种泛化显然是有局限性的,这种简单的设计必然只能实现in-domain的泛化,并且对于大规模训练可能效果不佳,作者仅仅在Meta-world和Mujoco两个...