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pro vyhledávání: '"Pedersen, Joachim Winther"'
Autor:
Nisioti, Eleni, Glanois, Claire, Najarro, Elias, Dai, Andrew, Meyerson, Elliot, Pedersen, Joachim Winther, Teodorescu, Laetitia, Hayes, Conor F., Sudhakaran, Shyam, Risi, Sebastian
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on
Externí odkaz:
http://arxiv.org/abs/2407.09502
In biological evolution complex neural structures grow from a handful of cellular ingredients. As genomes in nature are bounded in size, this complexity is achieved by a growth process where cells communicate locally to decide whether to differentiat
Externí odkaz:
http://arxiv.org/abs/2405.08510
Artificial neural networks used for reinforcement learning are structurally rigid, meaning that each optimized parameter of the network is tied to its specific placement in the network structure. It also means that a network only works with pre-defin
Externí odkaz:
http://arxiv.org/abs/2404.15193
Biological nervous systems consist of networks of diverse, sophisticated information processors in the form of neurons of different classes. In most artificial neural networks (ANNs), neural computation is abstracted to an activation function that is
Externí odkaz:
http://arxiv.org/abs/2305.15945
Organisms in nature have evolved to exhibit flexibility in face of changes to the environment and/or to themselves. Artificial neural networks (ANNs) have proven useful for controlling of artificial agents acting in environments. However, most ANN mo
Externí odkaz:
http://arxiv.org/abs/2205.07868
Generalization to out-of-distribution (OOD) circumstances after training remains a challenge for artificial agents. To improve the robustness displayed by plastic Hebbian neural networks, we evolve a set of Hebbian learning rules, where multiple conn
Externí odkaz:
http://arxiv.org/abs/2104.07959