Noah Lewis
According to our database1,
Noah Lewis
authored at least 17 papers
between 2017 and 2024.
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Bibliography
2024
Safe Explanations And Explainable Models For Neuroimaging Data Through A Framework Of Constraints.
PhD thesis, 2024
Proceedings of the IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, 2024
2023
Interpretable LSTM model reveals transiently-realized patterns of dynamic brain connectivity that predict patient deterioration or recovery from very mild cognitive impairment.
Comput. Biol. Medicine, July, 2023
Proceedings of the IEEE International Symposium on Mixed and Augmented Reality Adjunct, 2023
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
Proceedings of the 20th IEEE International Symposium on Biomedical Imaging, 2023
2022
CommsVAE: Learning the brain's macroscale communication dynamics using coupled sequential VAEs.
CoRR, 2022
Spatio-temporally separable non-linear latent factor learning: an application to somatomotor cortex fMRI data.
CoRR, 2022
Going from lines to triangles: A formulation for time-frequency moments of time-series with application to study fMRI.
Proceedings of the 14th IEEE Image, Video, and Multidimensional Signal Processing Workshop, 2022
2021
Proceedings of the 9th IEEE International Conference on Healthcare Informatics, 2021
Multi-modal deep learning of functional and structural neuroimaging and genomic data to predict mental illness.
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
Proceedings of the 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society, 2021
2020
Proceedings of the Medical Image Computing and Computer Assisted Intervention - MICCAI 2020, 2020
Functional Multi-Connectivity: A Novel Approach To Assess Multi-Way Entanglement Between Networks and Voxels.
Proceedings of the 17th IEEE International Symposium on Biomedical Imaging, 2020
2017
Proceedings of the 2017 International Joint Conference on Neural Networks, 2017