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Found 6 papers, 0 papers with code
A newborn embodied Turing test for view-invariant object recognition
Recent progress in artificial intelligence has renewed interest in building machines that learn like animals.
Parallel development of social preferences in fish and machines
We found that when artificial fish had two core learning mechanisms (reinforcement learning and curiosity-driven learning), artificial fish developed fish-like social preferences.
Controlled-rearing studies of newborn chicks and deep neural networks
Thus, the CNNs were not more data hungry than animals: both CNNs and chicks successfully developed robust object models from training data of a single object.
Development of collective behavior in newborn artificial agents
Here, we used deep reinforcement learning and curiosity-driven learning -- two learning mechanisms deeply rooted in psychological and neuroscientific research -- to build newborn artificial agents that develop collective behavior.
Modeling Object Recognition in Newborn Chicks using Deep Neural Networks
What learning algorithms underlie object recognition in newborn brains?
Joint Interaction and Trajectory Prediction for Autonomous Driving using Graph Neural Networks
In this work, we aim to predict the future motion of vehicles in a traffic scene by explicitly modeling their pairwise interactions.
