Subject-specific fMRI embeddings learned unsupervised from the Natural Scenes Dataset can be aligned across individuals via orthogonal rotations, supporting a shared neural geometry in visual cortex.
ISBN 9781510860964
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Introduces statistically efficient estimators for Renyi-α, Tsallis-α, reverse and forward KL divergences with REINFORCE and score-matching control variates for faster GFlowNet training.
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Platonic Representations in the Human Brain: Unsupervised Recovery of Universal Geometry
Subject-specific fMRI embeddings learned unsupervised from the Natural Scenes Dataset can be aligned across individuals via orthogonal rotations, supporting a shared neural geometry in visual cortex.
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On Divergence Measures for Training GFlowNets
Introduces statistically efficient estimators for Renyi-α, Tsallis-α, reverse and forward KL divergences with REINFORCE and score-matching control variates for faster GFlowNet training.