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Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.

P. Guo, W. Morningstar, R. Vemulapalli, K. Singhal, V. Patel, and P. Mansfield. CoRR, (2023)

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Explicit discriminative representation for improved classification of manifold features.A. Wiliem, R. Vemulapalli, and B. Lovell. Pattern Recognit. Lett., (2016)Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.P. Guo, W. Morningstar, R. Vemulapalli, K. Singhal, V. Patel, and P. Mansfield. CoRR, (2023)Weight subcloning: direct initialization of transformers using larger pretrained ones.M. Samragh, M. Farajtabar, S. Mehta, R. Vemulapalli, F. Faghri, D. Naik, O. Tuzel, and M. Rastegari. CoRR, (2023)Federated Training of Dual Encoding Models on Small Non-IID Client Datasets.R. Vemulapalli, W. Morningstar, P. Mansfield, H. Eichner, K. Singhal, A. Afkanpour, and B. Green. CoRR, (2022)Balancing Constraints and Submodularity in Data Subset Selection.S. Ramalingam, D. Glasner, K. Patel, R. Vemulapalli, S. Jayasumana, and S. Kumar. CoRR, (2021)Boosting Image-based Mutual Gaze Detection using Pseudo 3D Gaze.B. Doosti, C. Chen, R. Vemulapalli, X. Jia, Y. Zhu, and B. Green. AAAI, page 1273-1281. AAAI Press, (2021)Rolling Rotations for Recognizing Human Actions from 3D Skeletal Data.R. Vemulapalli, and R. Chellappa. CVPR, page 4471-4479. IEEE Computer Society, (2016)Unsupervised Cross-Modal Synthesis of Subject-Specific Scans.R. Vemulapalli, H. Nguyen, and S. Zhou. ICCV, page 630-638. IEEE Computer Society, (2015)Label-efficient Training of Small Task-specific Models by Leveraging Vision Foundation Models.R. Vemulapalli, H. Pouransari, F. Faghri, S. Mehta, M. Farajtabar, M. Rastegari, and O. Tuzel. CoRR, (2023)CLIP meets Model Zoo Experts: Pseudo-Supervision for Visual Enhancement.M. Salehi, M. Farajtabar, M. Horton, F. Faghri, H. Pouransari, R. Vemulapalli, O. Tuzel, A. Farhadi, M. Rastegari, and S. Mehta. CoRR, (2023)

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Disambiguation of "Vemulapalli, Raviteja"

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Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.

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copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.

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Towards Federated Learning Under Resource Constraints via Layer-wise Training and Depth Dropout.