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Found 5 papers, 1 papers with code
Time-inhomogeneous diffusion geometry and topology
From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel.
Learning shared neural manifolds from multi-subject FMRI data
In order to understand the connection between stimuli of interest and brain activity, and analyze differences and commonalities between subjects, it becomes important to learn a meaningful embedding of the data that denoises, and reveals its intrinsic structure.
Exploring the Geometry and Topology of Neural Network Loss Landscapes
This suggests that qualitative and quantitative examination of the loss landscape geometry could yield insights about neural network generalization performance during training.
TrajectoryNet: A Dynamic Optimal Transport Network for Modeling Cellular Dynamics
To address this issue, we establish a link between continuous normalizing flows and dynamic optimal transport, that allows us to model the expected paths of points over time.
Learning Safe Policies with Expert Guidance
We propose a framework for ensuring safe behavior of a reinforcement learning agent when the reward function may be difficult to specify.
