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pro vyhledávání: '"Lucas, Christopher P."'
Autor:
Gyevnar, Balint, Droop, Stephanie, Quillien, Tadeg, Cohen, Shay B., Bramley, Neil R., Lucas, Christopher G., Albrecht, Stefano V.
Cognitive science can help us understand which explanations people might expect, and in which format they frame these explanations, whether causal, counterfactual, or teleological (i.e., purpose-oriented). Understanding the relevance of these concept
Externí odkaz:
http://arxiv.org/abs/2403.08828
Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share characteristics with the ones they have encountered during training. In this wo
Externí odkaz:
http://arxiv.org/abs/2402.03479
Transfer learning for Bayesian optimisation has generally assumed a strong similarity between optimisation tasks, with at least a subset having similar optimal inputs. This assumption can reduce computational costs, but it is violated in a wide range
Externí odkaz:
http://arxiv.org/abs/2311.14653
Autor:
Dankers, Verna, Lucas, Christopher G.
When natural language phrases are combined, their meaning is often more than the sum of their parts. In the context of NLP tasks such as sentiment analysis, where the meaning of a phrase is its sentiment, that still applies. Many NLP studies on senti
Externí odkaz:
http://arxiv.org/abs/2310.20656
To successfully navigate its environment, an agent must construct and maintain representations of the other agents that it encounters. Such representations are useful for many tasks, but they are not without cost. As a result, agents must make decisi
Externí odkaz:
http://arxiv.org/abs/2310.04852
A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even when these share similar characteristics with environments encountered d
Externí odkaz:
http://arxiv.org/abs/2310.03494
Infants expect physical objects to be rigid and persist through space and time and in spite of occlusion. Developmentists frequently attribute these expectations to a "core system" for object recognition. However, it is unclear if this move is necess
Externí odkaz:
http://arxiv.org/abs/2309.07099
DreamCoder is an inductive program synthesis system that, whilst solving problems, learns to simplify search in an iterative wake-sleep procedure. The cost of search is amortized by training a neural search policy, reducing search breadth and effecti
Externí odkaz:
http://arxiv.org/abs/2306.07856
Bayesian optimisation is a powerful method for optimising black-box functions, popular in settings where the true function is expensive to evaluate and no gradient information is available. Bayesian optimisation can improve responses to many optimisa
Externí odkaz:
http://arxiv.org/abs/2306.04343
Autor:
Valentin, Simon, Kleinegesse, Steven, Bramley, Neil R., Seriès, Peggy, Gutmann, Michael U., Lucas, Christopher G.
Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and abil
Externí odkaz:
http://arxiv.org/abs/2305.07721