Machine learning and deep learning in smart manufacturing: The smart grid paradigm
Industry 4.0 is the new industrial revolution. By connecting every machine and activity
through network sensors to the Internet, a huge amount of data is generated. Machine …
through network sensors to the Internet, a huge amount of data is generated. Machine …
An algorithmic perspective on imitation learning
As robots and other intelligent agents move from simple environments and problems to more
complex, unstructured settings, manually programming their behavior has become …
complex, unstructured settings, manually programming their behavior has become …
Model-based reinforcement learning: A survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
Groundwater level prediction using machine learning algorithms in a drought-prone area
Groundwater resources (GWR) play a crucial role in agricultural crop production, daily life,
and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in …
and economic progress. Therefore, accurate prediction of groundwater (GW) level will aid in …
Making ai forget you: Data deletion in machine learning
Intense recent discussions have focused on how to provide individuals with control over
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …
when their data can and cannot be used---the EU's Right To Be Forgotten regulation is an …
Few-shot image recognition by predicting parameters from activations
In this paper, we are interested in the few-shot learning problem. In particular, we focus on a
challenging scenario where the number of categories is large and the number of examples …
challenging scenario where the number of categories is large and the number of examples …
Continuous deep q-learning with model-based acceleration
Abstract Model-free reinforcement learning has been successfully applied to a range of
challenging problems, and has recently been extended to handle large neural network …
challenging problems, and has recently been extended to handle large neural network …
Survey of model-based reinforcement learning: Applications on robotics
Reinforcement learning is an appealing approach for allowing robots to learn new tasks.
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …
Relevant literature reveals a plethora of methods, but at the same time makes clear the lack …
Robot learning system based on adaptive neural control and dynamic movement primitives
This paper proposes an enhanced robot skill learning system considering both motion
generation and trajectory tracking. During robot learning demonstrations, dynamic …
generation and trajectory tracking. During robot learning demonstrations, dynamic …