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Found 8 papers, 4 papers with code
Graph Neural Tangent Kernel: Convergence on Large Graphs
We use graphons to define limit objects -- graphon NNs for GNNs, and graphon NTKs for GNTKs -- , and prove that, on a sequence of graphs, the GNTKs converge to the graphon NTK.
Encoding priors in the brain: a reinforcement learning model for mouse decision making
The grating is often low in contrast which makes the task relatively difficult, and the prior probability that the grating appears on the right is either 80% or 20%, in (unsignaled) blocks of about 50 trials.
Stroke recovery phenotyping through network trajectory approaches and graph neural networks
To demonstrate the utility of this approach, we analyzed data from the NINDS tPA trial using the Trajectory Profile Clustering (TPC) method to identify distinct stroke recovery patterns for 11 different neurological domains at 5 discrete time points.
Encoded Prior Sliced Wasserstein AutoEncoder for learning latent manifold representations
While variational autoencoders have been successful in several tasks, the use of conventional priors are limited in their ability to encode the underlying structure of input data.
Multi-layer Trajectory Clustering: A Network Algorithm for Disease Subtyping
Many diseases display heterogeneity in clinical features and their progression, indicative of the existence of disease subtypes.
Separation of Chaotic Signals by Reservoir Computing
Indeed, our method works well when the component frequency spectra are indistinguishable - a case where a Wiener filter performs essentially no separation.
Identifying and Predicting Parkinson's Disease Subtypes through Trajectory Clustering via Bipartite Networks
In summary, using PD as a model for chronic progressive diseases, we show that TPC leverages high-dimensional longitudinal datasets for subtype identification and early prediction of individual disease subtype.
Applications Dynamical Systems Biological Physics Data Analysis, Statistics and Probability
Generalization of Learning using Reservoir Computing
Thus, as opposed to training the entire high dimensional reservoir state, the reservoir only needs to train on these unique relationships, allowing the reservoir to perform well with very few training examples.
