The vision of intelligent and fully autonomous robots, which are part of our daily lives and automatically learn from mistakes and adapt to new situations, has been around for many decades. However, this vision has been elusive so far. Although …
Reinforcement learning (RL) is a mathematical framework for learning from trial and error, which makes it an appealing framework for intelligent systems and autonomous learning. RL has had many success stories recently, but it is typically data …
Modern machine learning algorithms often require us to solve meta challenges, such as setting the learning rate in stochastic gradient descent, the margin in an SVM, or the depth of a neural network. Finding good values for these hyperparameters is …
High-impact areas of machine learning and AI, such as personalized healthcare, autonomous robots, or environmental science share some practical challenges: They are either small-data problems or a small collection of big-data problems. Therefore, …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …
On our path toward fully autonomous systems, i.e., systems that operate in the real world without significant human intervention, reinforcement learning (RL) is a promising framework for learning to solve problems by trial and error. While RL has had …