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Published in ICLR, AI4 Developing Countries., 2020
We design and test an equivariant version of MobileNetV2 and further optimize it with model quantization to enable more efficient inference.
Recommended citation: Mohamed, Mirgahney, et al. "A data and compute efficient design for limited-resources deep learning." ICLR, AI4 Developing Countries. (2020). https://arxiv.org/abs/2004.09691.pdf
Published in , 2022
This paper is about the number 1. The number 2 is left for future work.
Recommended citation: Mohamed, Mirgahney et.al. (2021). "Data and compute efficient equivariant convolutional networks; US Patent App. 17/170,745. https://patentimages.storage.googleapis.com/d8/3b/56/ab431403304b8c/US20210248467A1.pdf
Published in 3DV, 2022
We propose Geometric Neural Parametric Models (GNPM), a learned parametric model that takes into account the local structure of data to learn disentangled shape and pose latent spaces of 4D dynamics, using a geometric-aware architecture on point clouds.
Recommended citation: Mirgahney Mohamed, Lourdes Agapito (2022). "GNPM: Geometric-Aware Neural Parametric Models." 3DV. 1(1). https://arxiv.org/abs/2209.10621.pdf
Undergraduate course, University 1, Department, 2014
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Workshop, University 1, Department, 2015
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