How variability shapes learning and generalization
Learning is using past experiences to inform new behaviors and actions. Because all
experiences are unique, learning always requires some generalization. An effective way of …
experiences are unique, learning always requires some generalization. An effective way of …
Deep reinforcement learning and its neuroscientific implications
The emergence of powerful artificial intelligence (AI) is defining new research directions in
neuroscience. To date, this research has focused largely on deep neural networks trained …
neuroscience. To date, this research has focused largely on deep neural networks trained …
Interactive language: Talking to robots in real time
We present a framework for building interactive, real-time, natural language-instructable
robots in the real world, and we open source related assets (dataset, environment …
robots in the real world, and we open source related assets (dataset, environment …
The next decade in AI: four steps towards robust artificial intelligence
G Marcus - arXiv preprint arXiv:2002.06177, 2020 - arxiv.org
Recent research in artificial intelligence and machine learning has largely emphasized
general-purpose learning and ever-larger training sets and more and more compute. In …
general-purpose learning and ever-larger training sets and more and more compute. In …
Language conditioned imitation learning over unstructured data
C Lynch, P Sermanet - arXiv preprint arXiv:2005.07648, 2020 - arxiv.org
Natural language is perhaps the most flexible and intuitive way for humans to communicate
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
tasks to a robot. Prior work in imitation learning typically requires each task be specified with …
Autotelic agents with intrinsically motivated goal-conditioned reinforcement learning: a short survey
Building autonomous machines that can explore open-ended environments, discover
possible interactions and build repertoires of skills is a general objective of artificial …
possible interactions and build repertoires of skills is a general objective of artificial …
The nethack learning environment
Abstract Progress in Reinforcement Learning (RL) algorithms goes hand-in-hand with the
development of challenging environments that test the limits of current methods. While …
development of challenging environments that test the limits of current methods. While …
Compositional generalization in semantic parsing: Pre-training vs. specialized architectures
While mainstream machine learning methods are known to have limited ability to
compositionally generalize, new architectures and techniques continue to be proposed to …
compositionally generalize, new architectures and techniques continue to be proposed to …
Self-supervised learning through the eyes of a child
Within months of birth, children develop meaningful expectations about the world around
them. How much of this early knowledge can be explained through generic learning …
them. How much of this early knowledge can be explained through generic learning …
Neuroevolution of self-interpretable agents
Inattentional blindness is the psychological phenomenon that causes one to miss things in
plain sight. It is a consequence of the selective attention in perception that lets us remain …
plain sight. It is a consequence of the selective attention in perception that lets us remain …