… of learning and have generated new findings related to the neurological processes involved
in learning, individual and cultural variability related to learning… mechanisms of learning and …

… we directly optimize to learn without forgetting. In other words, we harness meta-learning
to allow the network to learn to continually learn. However, instead of vanilla meta-learning (eg …

… : we may expect learning to be better if we learn it. Recent research in machine learning has
… We should thus expect this to be relevant for neuroscience: how could the correct learning …

… learning) or maximises reward (reinforcement learning) (Box 2, i). For example, this is the
basis of neural network learning … strengths, hence connectionist learning) which have been …

… a system that is able to learn both visual and audio semantic … -Visual Correspondence (AVC)
learning task that is used to … In terms of prior work, the most closely related deep learning …

In recent years deep reinforcement learning (RL) systems have attained superhuman
performance in a number of challenging task domains. However, a major limitation of such …

… transfer learning that optimizes for transferability and fast learning. This class of methods
explicitly trains for the ability to learn new concepts, or learns how to learn. While the concept of …

… We demonstrate, for the first time, that a single deep learning model can jointly learn a …
general deep learning architectures, especially since our model shows transfer learning from …

… We sense that motivation to learn exists (as opposed to being a euphemism, intellectual …
lations to enhance learning)3–5. But what do we really mean by motivation to learn, and how …

… We present a method for learning a generative 3D model based on neural radiance fields, …
on a shared latent space, you can learn a space of radiance fields that models shape and …