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Optimal testing using combined test statistics across independent studies
We introduce a natural and mild restriction on the meta-level combination functions of the local trials.
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Scaling Riemannian Diffusion Models
Riemannian diffusion models draw inspiration from standard Euclidean space diffusion models to learn distributions on general manifolds.
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On the Role of Noise in the Sample Complexity of Learning Recurrent Neural Networks: Exponential Gaps for Long Sequences Anonymous Author(s) Affiliation Address email
Recurrent Neural Networks (RNNs) are effective tools for processing sequential data.
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Accelerating Motion Planning via Optimal Transport
Motion planning is still an open problem for many disciplines, e.g., robotics, autonomous driving, due to their need for high computational resources that hinder
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