!!Michael Bronstein - Selected Publications
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P. Gainza, F. Sverrisson, F. Monti, E. Rodolà, D. Boscaini, M. M. Bronstein, B. E. Correia, Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning, Nature Methods 17:184–192(2020) \\
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L. Cosmo, M. Panine, A. Rampini, M. Ovsjanikov, M. M. Bronstein, E. Rodolà, Isospectralization, or how to hear shape, style, and correspondence, Proc. CVPR (2019)\\
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Y. Wang, Y. Sun, Z. Liu, S. E. Sarma, M. M. Bronstein, J. M. Solomon, Dynamic graph CNN for learning on point clouds, ACM Trans. Graphics 38(5):1-12 (2019) \\
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F. Monti, M. M. Bronstein, X. Bresson, Geometric matrix completion with recurrent multi-graph neural networks, Proc. NIPS (2018)\\
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M. M. Bronstein, J. Bruna, Y. LeCun, A. Szlam, P. Vandergheynst, Geometric deep learning: going beyond Euclidean data, IEEE Signal Processing Magazine 34(4):18-42 (2017) \\
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F. Monti, D. Boscaini, J. Masci, E. Rodolà, J. Svoboda, M. M. Bronstein, Geometric deep learning on graphs and manifolds using mixture model CNNs, Proc. CVPR (2017) \\
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E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, D. Cremers, Partial functional correspondence, Computer Graphics Forum 36(1):222-236 (2017) \\
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A. M. Bronstein, M. M. Bronstein, R. Kimmel, Numerical geometry of non-rigid shapes, Springer Verlag (2008) \\
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A. M. Bronstein, M. M. Bronstein, R. Kimmel, Generalized multidimensional scaling: a framework for isometry-invariant partial surface matching, PNAS 103(5):1168-1172 (2006)\\
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A. M. Bronstein, M. M. Bronstein, R. Kimmel, Three-dimensional face recognition, Int. J. Computer Vision, 64(1):5-30 (2005)