In machine learning, the model is not as complicated as possible. Good generalization
ability means that the model not only performs well on the training data set, but also can …

The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …

While deep reinforcement learning (RL) has fueled multiple high-profile successes in
machine learning, it is held back from more widespread adoption by its often poor data …

Reinforcement learning (RL) is a machine learning approach that trains agents to maximize
cumulative rewards through interactions with environments. The integration of RL with deep …

Abstract Machine learning research has advanced in multiple aspects, including model
structures and learning methods. The effort to automate such research, known as AutoML …

Reinforcement learning (RL) algorithms update an agent's parameters according to one of
several possible rules, discovered manually through years of research. Automating the …

Tremendous progress has been made in reinforcement learning (RL) over the past decade.
Most of these advancements came through the continual development of new algorithms …

Meta-learning empowers artificial intelligence to increase its efficiency by learning how to
learn. Unlocking this potential involves overcoming a challenging meta-optimisation …

Fine-grained image recognition is a longstanding computer vision challenge that focuses on
differentiating objects belonging to multiple subordinate categories within the same meta …

We propose a method for meta-learning reinforcement learning algorithms by searching
over the space of computational graphs which compute the loss function for a value-based …