| Machine learning in resting-state fMRI analysis M Khosla, K Jamison, GH Ngo, A Kuceyeski, MR Sabuncu Magnetic resonance imaging 64, 101-121, 2019 | 205 | 2019 |
| Ensemble learning with 3D convolutional neural networks for functional connectome-based prediction M Khosla, K Jamison, A Kuceyeski, MR Sabuncu NeuroImage 199, 651-662, 2019 | 103 | 2019 |
| 3D convolutional neural networks for classification of functional connectomes M Khosla, K Jamison, A Kuceyeski, MR Sabuncu International Workshop on Deep Learning in Medical Image Analysis, 137-145, 2018 | 96 | 2018 |
| Using artificial neural networks to ask ‘why’questions of minds and brains N Kanwisher, M Khosla, K Dobs Trends in Neurosciences 46 (3), 240-254, 2023 | 74 | 2023 |
| Cortical response to naturalistic stimuli is largely predictable with deep neural networks M Khosla, GH Ngo, K Jamison, A Kuceyeski, MR Sabuncu Science Advances 7 (22), eabe7547, 2021 | 42 | 2021 |
| A highly selective response to food in human visual cortex revealed by hypothesis-free voxel decomposition M Khosla, NAR Murty, N Kanwisher Current Biology 32 (19), 4159-4171. e9, 2022 | 41 | 2022 |
| A switch and wave of neuronal activity in the cerebral cortex during the first second of conscious perception WX Herman, RE Smith, SI Kronemer, RE Watsky, WC Chen, LM Gober, ... Cerebral Cortex 29 (2), 461-474, 2019 | 29 | 2019 |
| Neurogen: activation optimized image synthesis for discovery neuroscience Z Gu, KW Jamison, M Khosla, EJ Allen, Y Wu, G St-Yves, T Naselaris, ... NeuroImage 247, 118812, 2022 | 25 | 2022 |
| Revalidation of the Sat-Chit-Ananda Scale K Singh, P Khanna, M Khosla, M Rapelly, A Soni Journal of religion and health 57, 1392-1401, 2018 | 22 | 2018 |
| Predicting individual task contrasts from resting‐state functional connectivity using a surface‐based convolutional network GH Ngo, M Khosla, K Jamison, A Kuceyeski, MR Sabuncu NeuroImage 248, 118849, 2022 | 20 | 2022 |
| High-level visual areas act like domain-general filters with strong selectivity and functional specialization M Khosla, L Wehbe bioRxiv, 2022.03. 16.484578, 2022 | 10 | 2022 |
| Polarons explain luminescence behavior of colloidal quantum dots at low temperature M Khosla, S Rao, S Gupta Scientific Reports 8 (1), 8385, 2018 | 10 | 2018 |
| Detecting abnormalities in resting-state dynamics: an unsupervised learning approach M Khosla, K Jamison, A Kuceyeski, MR Sabuncu Machine Learning in Medical Imaging: 10th International Workshop, MLMI 2019 …, 2019 | 9 | 2019 |
| From connectomic to task-evoked fingerprints: Individualized prediction of task contrasts from resting-state functional connectivity GH Ngo, M Khosla, K Jamison, A Kuceyeski, MR Sabuncu Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 7 | 2020 |
| High-level visual areas act like domain-general filters with strong selectivity and functional specialization. bioRxiv M Khosla, L Wehbe | 5 | 2022 |
| Soft Matching Distance: A metric on neural representations that captures single-neuron tuning M Khosla, AH Williams Proceedings of UniReps: the First Workshop on Unifying Representations in …, 2024 | 4 | 2024 |
| Characterizing the ventral visual stream with response-optimized neural encoding models M Khosla, K Jamison, A Kuceyeski, M Sabuncu Advances in Neural Information Processing Systems 35, 9389-9402, 2022 | 4 | 2022 |
| Data-driven component modeling reveals the functional organization of high-level visual cortex M Khosla, NAR Murty, N Kanwisher Journal of Vision 22 (14), 4184-4184, 2022 | 3 | 2022 |
| Neural encoding with visual attention M Khosla, G Ngo, K Jamison, A Kuceyeski, M Sabuncu Advances in Neural Information Processing Systems 33, 15942-15953, 2020 | 3 | 2020 |
| A shared neural encoding model for the prediction of subject-specific fMRI response M Khosla, GH Ngo, K Jamison, A Kuceyeski, MR Sabuncu Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd …, 2020 | 3 | 2020 |
Mert R. SabuncuCornell University, Cornell Tech, Weill Cornell Medicine在 cornell.edu 的电子邮件经过验证
