| Effect of graphene on nonneuronal and neuronal cell viability and stress SK Rastogi, G Raghavan, G Yang, T Cohen-Karni Nano letters 17 (5), 3297-3301, 2017 | 80 | 2017 |
| Whole-cell scale dynamic organization of lysosomes revealed by spatial statistical analysis Q Ba, G Raghavan, K Kiselyov, G Yang Cell reports 23 (12), 3591-3606, 2018 | 69 | 2018 |
| Graphene microelectrode arrays for electrical and optical measurements of human stem cell-derived cardiomyocytes SK Rastogi, J Bliley, DJ Shiwarski, G Raghavan, AW Feinberg, ... Cellular and Molecular Bioengineering 11, 407-418, 2018 | 46 | 2018 |
| Feasibility of spatial frequency-domain imaging for monitoring palpable breast lesions CM Robbins, G Raghavan, JF Antaki, JM Kainerstorfer Journal of Biomedical Optics, 2017 | 18 | 2017 |
| Direct observation and analysis of the dynamics of the photoresponsive transcription factor GAL4 G Raghavan, K Hidaka, H Sugiyama, M Endo Angewandte Chemie International Edition 58 (23), 7626-7630, 2019 | 16 | 2019 |
| Neural networks grown and self-organized by noise G Raghavan, M Thomson Advances in Neural Information Processing Systems 32, 2019 | 11 | 2019 |
| Self-organization of multi-layer spiking neural networks G Raghavan, C Lin, M Thomson arXiv preprint arXiv:2006.06902, 2020 | 4 | 2020 |
| Engineering flexible machine learning systems by traversing functionally invariant paths in weight space G Raghavan, M Thomson arXiv preprint arXiv:2205.00334, 2022 | 2 | 2022 |
| Solving hybrid machine learning tasks by traversing weight space geodesics G Raghavan, M Thomson arXiv preprint arXiv:2106.02793, 2021 | 1 | 2021 |
| Sparsifying networks by traversing Geodesics G Raghavan, M Thomson arXiv preprint arXiv:2012.09605, 2020 | 1 | 2020 |
| Geometric algorithms for predicting resilience and recovering damage in neural networks G Raghavan, J Li, M Thomson arXiv preprint arXiv:2005.11603, 2020 | 1 | 2020 |
| Deep tissue multi-wavelength spatial frequency domain imaging CM Robbins, G Raghavan, J Yang, JF Antaki, JM Kainerstorfer Optical Tomography and Spectroscopy, JTu3A. 41, 2018 | 1 | 2018 |
| Engineering artificial systems with natural intelligence G Raghavan California Institute of Technology, 2023 | | 2023 |
| Flexible machine learning MW Thomson, G Raghavan US Patent App. 17/826,876, 2022 | | 2022 |
| Resilience determination and damage recovery in neural networks G Raghavan, MW Thomson US Patent App. 17/349,743, 2021 | | 2021 |
| Self organization of neuromorphic machine learning architectures G Raghavan, MW Thomson, C Lin US Patent App. 17/127,762, 2021 | | 2021 |
| Architecture Agnostic Neural Networks S Talukder, G Raghavan, Y Yue arXiv preprint arXiv:2011.02712, 2020 | | 2020 |
| Cytoskeleton-Mediated Dynamic Organization of Lysosomes Promotes Their Interactions with Endosomes Q Ba, G Raghavan, K Kiselyov, G Yang Biophysical Journal 114 (3), 656a, 2018 | | 2018 |
| Computational Analysis of Whole-Cell Scale Distributions and Interactions of Intracellular Organelle Networks Q Ba, G Raghavan, HC Lee, G Yang Biophysical Journal 112 (3), 238a, 2017 | | 2017 |
| Navigating the Challenges of Fine Tuning and Catastrophic Forgetting Published on Mar 25, 2024 G Raghavan | | |
Ge YangProfessor, University of Chinese Academy of Sciences & National Laboratory of Pattern Recognition在 ucas.edu.cn 的电子邮件经过验证
