Artikel dengan mandat akses publik - Paul BendichPelajari lebih lanjut
Tidak tersedia di mana pun: 1
Upstream fusion of multiple sensing modalities using machine learning and topological analysis: An initial exploration
D Garagić, J Peskoe, F Liu, MS Claffey, P Bendich, J Hineman, ...
2018 IEEE Aerospace Conference, 1-8, 2018
Mandat: US Department of Defense
Tersedia di suatu tempat: 18
Persistent homology analysis of brain artery trees
P Bendich, JS Marron, E Miller, A Pieloch, S Skwerer
Annals of Applied Statistics 10 (1), 198-218, 2016
Mandat: US National Science Foundation, US National Institutes of Health
Local homology transfer and stratification learning
P Bendich, B Wang, S Mukherjee
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete …, 2012
Mandat: US National Institutes of Health
Topological and statistical behavior classifiers for tracking applications
AWT Paul Bendich, Sang Chin, Jesse Clarke, John deSena, John Harer ...
IEEE Transactions on Aerospace and Electronic Systems 52 (6), 2644-2661, 2016
Mandat: US National Science Foundation
A fast and robust method for global topological functional optimization
Y Solomon, A Wagner, P Bendich
International Conference on Artificial Intelligence and Statistics, 109-117, 2021
Mandat: US National Science Foundation, US Department of Defense
Stabilizing the unstable output of persistent homology computations
P Bendich, P Bubenik, A Wagner
Journal of Applied and Computational Topology 4 (2), 309-338, 2020
Mandat: US National Science Foundation, US Department of Defense
Stratification Learning through Homology Inference.
P Bendich, S Mukherjee, B Wang
AAAI Fall Symposium: Manifold Learning and Its Applications, 2010
Mandat: US National Institutes of Health
Convolutional persistence transforms
YE Solomon, P Bendich
Journal of Applied and Computational Topology, 1-33, 2024
Mandat: US Department of Defense
Multi-scale geometric summaries for similarity-based sensor fusion
CJ Tralie, P Bendich, J Harer
2019 IEEE Aerospace Conference, 1-10, 2019
Mandat: US National Science Foundation, US Department of Defense
Geometric cross-modal comparison of heterogeneous sensor data
CJ Tralie, A Smith, N Borggren, J Hineman, P Bendich, P Zulch, J Harer
2018 IEEE Aerospace Conference, 1-10, 2018
Mandat: US National Science Foundation, US Department of Defense
Persistent obstruction theory for a model category of measures with applications to data merging
A Smith, P Bendich, J Harer
Transactions of the American Mathematical Society, Series B 8 (1), 1-38, 2021
Mandat: US Department of Defense
Topological parallax: A geometric specification for deep perception models
A Smith, M Catanzaro, G Angeloro, N Patel, P Bendich
Advances in Neural Information Processing Systems 36, 28155-28172, 2023
Mandat: US Department of Defense
Topological Simplification of Signals for Inference and Approximate Reconstruction
G Koplik, N Borggren, S Voisin, G Angeloro, J Hineman, T Johnson, ...
2023 IEEE Aerospace Conference, 1-11, 2023
Mandat: US Department of Defense
Geometric fusion via joint delay embeddings
E Solomon, P Bendich
2020 IEEE 23rd International Conference on Information Fusion (FUSION), 1-8, 2020
Mandat: US Department of Defense
Graph spectral embedding for parsimonious transmission of multivariate time series
L Yao, P Bendich
2020 IEEE Aerospace Conference, 1-12, 2020
Mandat: US Department of Defense
Geometric models for musical audio data
P Bendich, E Gasparovic, J Harer, C Tralie
32nd International Symposium on Computational Geometry (SoCG 2016), 65: 1-65: 5, 2016
Mandat: US National Science Foundation
Topological Decompositions Enhance Efficiency of Reinforcement Learning
MJ Catanzaro, A Dharna, J Hineman, JB Polly, K McGoff, AD Smith, ...
2024 IEEE Aerospace Conference, 1-8, 2024
Mandat: US Department of Defense
Topological Feature Tracking for submesoscale eddies
S Voisin, J Hineman, JB Polly, G Koplik, K Ball, P Bendich, J D ‘Addezio, ...
Geophysical research letters 49 (20), e2022GL099416, 2022
Mandat: US Department of Defense
Automation is all you need: Faster Earth system models with AI/ML
K Ball, J Hineman, S Voisin, G Koplik, P Bendich
Artificial Intelligence for Earth System Predictability (AI4ESP …, 2021
Mandat: US Department of Energy
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