D. Anguelov, P. Srinivasan, D. Koller, S. Thrun, J. Rodgers, and J. Davis. SCAPE: shape completion and animation of people. Trans. Graphics, pages 408--416, 2005.

Digital Library

Google Scholar

[6]

M. Aubry, U. Schlickewei, and D. Cremers. The wave kernel signature: A quantum mechanical approach to shape analysis. In Proc. 4DMOD, 2011.

Crossref

Google Scholar

[7]

P. Baldi and P. Sadowski. The dropout learning algorithm. Artificial Intelligence, 210:78--122, 2014.

Crossref

Google Scholar

[8]

F. Bogo, M. J. Black, M. Loper, and J. Romero. Detailed full-body reconstructions of moving people from monocular RGB-D sequences. In Proc. ICCV, 2015.

Digital Library

Google Scholar

[9]

F. Bogo, J. Romero, M. Loper, and M. J. Black. FAUST: Dataset and evaluation for 3D mesh registration. In Proc. CVPR, 2014.

Digital Library

Google Scholar

[10]

D. Boscaini, J. Masci, S. Melzi, M. M. Bronstein, U. Castellani, and P. Vandergheynst. Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks. Computer Graphics Forum, 34(5):13--23, 2015.

Crossref

Google Scholar

[11]

D. Boscaini, J. Masci, E. Rodolà, and M. Bronstein. Learning shape correspondence with anisotropic convolutional neural networks. In Proc. NIPS, 2016.

Digital Library

Google Scholar

[12]

D. Boscaini, J. Masci, E. Rodolà, M. Bronstein, and D. Cremers. Anisotropic diffusion descriptors. Computer Graphics Forum, 35(2):431--441, 2016.

Crossref

Google Scholar

[13]

L. Breiman. Random forests. In Machine Learning, volume 45, pages 5--32, 2001.

Digital Library

Google Scholar

[14]

J. Bromley et al. Signature verification using a "Siamese" time delay neural network. In Proc. NIPS. 1994.

Digital Library

Google Scholar

[15]

J. Bruna, W. Zaremba, A. Szlam, and Y. LeCun. Spectral networks and locally connected networks on graphs. In Proc. ICLR, 2014.

Google Scholar

[16]

Q. Chen and V. Koltun. Robust nonrigid registration by convex optimization. In IEEE ICCV, 2015.

Digital Library

Google Scholar

[17]

A. Choromanska, M. Henaff, M. Mathieu, G. B. Arous, and Y. LeCun. The loss surfaces of multilayer networks. In Proc. AISTATS, 2015.

Google Scholar

[18]

D. Clevert, T. Unterthiner, and S. Hochreiter. Fast and accurate deep network learning by exponential linear units (elus). In Proc. ICLR, 2015.

Google Scholar

[19]

L. Cosmo, E. Rodolà, M. M. Bronstein, et al. SHREC'16: Partial matching of deformable shapes. In Proc. 3DOR, 2016.

Google Scholar

[20]

L. Cosmo, E. Rodolà, J. Masci, A. Torsello, and M. Bronstein. Matching deformable objects in clutter. In Proc. 3DV, 2016.

Crossref

Google Scholar

[21]

A. Criminisi, J. Shotton, and E. Konukoglu. Decision forests: A unified framework for classification, regression, density estimation, manifold learning and semi-supervised learning. Foundations and Trends in Computer Graphics and Vision, 7(2-3):81--227, 2012.

Digital Library

Google Scholar

[22]

R. Dechter. Learning while searching in constraint-satisfaction-problems. In Proc. AAAI, 1986.

Digital Library

Google Scholar

[23]

J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei. ImageNet: A large-scale hierarchical image database. In Proc. CVPR, 2009.

Crossref

Google Scholar

[24]

A. Dosovitskiy, P. Fischer, E. Ilg, P. Hausser, C. Hazirbas, V. Golkov, P. van der Smagt, D. Cremers, and T. Brox. FlowNet: Learning optical flow with convolutional networks. In Proc. ICCV, 2015.

Digital Library

Google Scholar

[25]

A. Dosovitskiy, J. T. Springenberg, and T. Brox. Learning to generate chairs with convolutional neural networks. In Proc. CVPR, 2015.

Crossref

Google Scholar

[26]

M. Dou, J. Taylor, H. Fuchs, A. Fitzgibbon, and S. Izadi. 3d scanning deformable objects with a single rgbd sensor. In IEEE CVPR, 2015.

Crossref

Google Scholar

[27]

D. Erhan, Y. Bengio, A. Courville, P.-A. Manzagol, P. Vincent, and S. Bengio. Why does unsupervised pre-training help deep learning? JMLR, 11, 2010.

Digital Library

Google Scholar

[28]

K. Fukushima. Neocognitron: A self-organizing neural network for a mechanism of pattern recognition unaffected by shift in position. Biological Cybernetics, 36(4):193--202, 1980.

Crossref

Google Scholar

[29]

J. Gall, A. Yao, N. Razavi, L. Van Gool, and V. Lempitsky. Hough forests for object detection, tracking, and action recognition. Trans. PAMI, 33(11):2188--2202, 2011.

Digital Library

Google Scholar

[30]

X. Glorot and Y. Bengio. Understanding the difficulty of training deep feedforward neural networks. In Proc. Conf. Artificial Intelligence and Statistics, 2010.

Google Scholar

[31]

I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio. Generative adversarial nets. In Proc. NIPS. 2014.

Digital Library

Google Scholar

[32]

K. Gregor, I. Danihelka, A. Graves, D. J. Rezende, and D. Wierstra. DRAW: A recurrent neural network for image generation. In Proc. ICML, 2015.

Google Scholar

[33]

X. Han, T. Leung, Y. Jia, R. Sukthankar, and A. C. Berg. MatchNet: Unifying feature and metric learning for patch-based matching. In Proc. CVPR, 2015.

Google Scholar

[34]

K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. arXiv:1512.03385, 2015.

Google Scholar

[35]

K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification. In Proc. ICCV, 2015.

Digital Library

Google Scholar

[36]

G. E. Hinton. Training products of experts by minimizing contrastive divergence. Neural Computation, 14(8):1771--1800, 2002.

Digital Library

Google Scholar

[37]

G. E. Hinton, S. Osindero, and Y.-W. Teh. A fast learning algorithm for deep belief nets. Neural Computation, 18(7):1527--1554, July 2006.

Digital Library

Google Scholar

[38]

G. E. Hinton and R. R. Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504--507, 2006.

Crossref

Google Scholar

[39]

G. E. Hinton, N. Srivastava, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov. Improving neural networks by preventing co-adaptation of feature detectors. arXiv:1207.0580, 2012.

Google Scholar

[40]

S. Hochreiter and J. Schmidhuber. Simplifying neural nets by discovering flat minima. In Proc. NIPS, 1995.

Digital Library

Google Scholar

[41]

S. Hochreiter and J. Schmidhuber. Long Short-Term Memory. Neural Computation, 9(8):1735--1780, 1997.

Digital Library

Google Scholar

[42]

Q. Huang, B. Adams, M. Wicke, and L. J. Guibas. Non-rigid registration under isometric deformations. In Proc. SGP, 2008.

Digital Library

Google Scholar

[43]

S. I. and C. S. Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv:1502.03167, 2015.

Google Scholar

[44]

M. Jaderberg, K. Simonyan, A. Zisserman, and K. Kavukcuoglu. Spatial transformer networks. In Proc. NIPS, 2015.

Digital Library

Google Scholar

[45]

K. Kawaguchi. Deep learning without poor local minima. In Proc. NIPS, 2016.

Google Scholar

[46]

V. G. Kim, Y. Lipman, and T. Funkhouser. Blended Intrinsic Maps. Trans. Graphics, 30(4), 2011.

Digital Library

Google Scholar

[47]

D. P. Kingma, T. Salimans, and M. Welling. Variational dropout and the local reparameterization trick. In Proc. NIPS. 2015.

Digital Library

Google Scholar

[48]

A. Krizhevsky, I. Sutskever, and G. E. Hinton. Imagenet classification with deep convolutional neural networks. In Proc. NIPS. 2012.

Digital Library

Google Scholar

[49]

Z. Lähner, E. Rodolà, M. M. Bronstein, et al. SHREC'16: Matching of deformable shapes with topological noise. In Proc. 3DOR, 2016.

Google Scholar

[50]

Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proc. IEEE, 86(11):2278--2324, 1998.

Crossref

Google Scholar

[51]

H. Lee, R. Grosse, R. Ranganath, and A. Y. Ng. Convolutional deep belief networks for scalable unsupervised learning of hierarchical representations. In Proc. ICML, 2009.

Digital Library

Google Scholar

[52]

H. Li, B. Adams, L. J. Guibas, and M. Pauly. Robust single-view geometry and motion reconstruction. Trans. Graphics, 28(5), 2009.

Digital Library

Google Scholar

[53]

H. Li, E. Vouga, A. Gudym, L. Luo, J. T. Barron, and G. Gusev. 3D self-portraits. Trans. Graphics, 32(6), 2013.

Digital Library

Google Scholar

[54]

Y. Lipman and T. Funkhouser. Möbius voting for surface correspondence. Trans. Graphics, 28(3), 2009.

Digital Library

Google Scholar

[55]

O. Litany, E. Rodolà, A. M. Bronstein, M. M. Bronstein, and D. Cremers. Non-rigid puzzles. Computer Graphics Forum, 35(5), 2016.

Google Scholar

[56]

R. Litman and A. M. Bronstein. Learning spectral descriptors for deformable shape correspondence. Trans. PAMI, 36(1):170--180, 2014.

Digital Library

Google Scholar

[57]

J. Long, E. Shelhamer, and T. Darrell. Fully convolutional networks for semantic segmentation. Proc. CVPR, 2015.

Crossref

Google Scholar

[58]

S. Mallat. A Wavelet Tour of Signal Processing. Academic Press, 2008.

Digital Library

Google Scholar

[59]

J. Masci, D. Boscaini, M. M. Bronstein, and P. Vandergheynst. Geodesic convolutional neural networks on riemannian manifolds. In Proc. 3dRR, 2015.

Digital Library

Google Scholar

[60]

J. Masci, U. Meier, D. Cireşan, and J. Schmidhuber. Stacked convolutional autoencoders for hierarchical feature extraction. In Proc. ICANN, 2011.

Digital Library

Google Scholar

[61]

D. Maturana and S. Scherer. VoxNet: A 3D convolutional neural network for real-time object recognition. In Conf. Intelligent Robots and Systems, 2015.

Digital Library

Google Scholar

[62]

V. Mnih, N. Heess, A. Graves, and K. Kavukcuoglu. Recurrent models of visual attention. In Proc. NIPS. 2014.

Digital Library

Google Scholar

[63]

R. A. Newcombe, D. Fox, and S. M. Seitz. DynamicFusion: Reconstruction and tracking of non-rigid scenes in real-time. In Proc. CVPR, 2015.

Crossref

Google Scholar

[64]

H. Noh, S. Hong, and B. Han. Learning deconvolution network for semantic segmentation. arXiv:1505.04366, 2015.

Google Scholar

[65]

K. Olszewski, J. J. Lim, S. Saito, and H. Li. High-fidelity facial and speech animation for VR HMDs. Trans. Graphics, 35(6), 2016.

Digital Library

Google Scholar

[66]

M. Ovsjanikov, M. Ben-Chen, J. Solomon, A. Butscher, and L. J. Guibas. Functional maps: a flexible representation of maps between shapes. Trans. Graphics, 31(4), 2012.

Digital Library

Google Scholar

[67]

V. Pham, C. Kermorvant, and J. Louradour. Dropout improves recurrent neural networks for handwriting recognition. arXiv:1312.4569, 2013.

Google Scholar

[68]

U. Pinkall and K. Polthier. Computing discrete minimal surfaces and their conjugates. Experimental Mathematics, 2(1):15--36, 1993.

Crossref

Google Scholar

[69]

A. Radford, L. Metz, and S. Chintala. Unsupervised representation learning with deep convolutional generative adversarial networks. In Proc. ICLR. 2016.

Google Scholar

[70]

E. Rodolà, L. Cosmo, M. M. Bronstein, A. Torsello, and D. Cremers. Partial functional correspondence. Computer Graphics Forum, 2016.

Digital Library

Google Scholar

[71]

E. Rodolà, S. Rota Bulò, T. Windheuser, M. Vestner, and D. Cremers. Dense non-rigid shape correspondence using random forests. In Proc. CVPR, 2014.

Digital Library

Google Scholar

[72]

D. E. Rumelhart, G. E. Hinton, and R. J. Williams. Learning representations by back-propagating errors. Nature, 323(6088):533--536, 1986.

Crossref

Google Scholar

[73]

S. Rusinkiewicz, B. Brown, and M. Kazhdan. 3D scan matching and registration. In Proc. ICCV Courses, 2005.

Google Scholar

[74]

R. M. Rustamov, M. Ovsjanikov, O. Azencot, M. Ben-Chen, F. Chazal, and L. J. Guibas. Map-based exploration of intrinsic shape differences and variability. Trans. Graphics, 32(4):72, 2013.

Digital Library

Google Scholar

[75]

S. Saito, T. Li, and H. Li. Real-time facial segmentation and performance capture from RGB input. In Proc. ECCV, 2016.

Crossref

Google Scholar

[76]

F. Schroff, D. Kalenichenko, and J. Philbin. Facenet: A unified embedding for face recognition and clustering. arXiv:1503.03832, 2015.

Google Scholar

[77]

P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun. OverFeat: Integrated recognition, localization and detection using convolutional networks. arXiv:1312.6229, 2013.

Google Scholar

[78]

J. Shotton, R. Girshick, A. Fitzgibbon, T. Sharp, M. Cook, M. Finocchio, R. Moore, P. Kohli, A. Criminisi, A. Kipman, and A. Blake. Efficient human pose estimation from single depth images. Trans. PAMI, 35(12):2821--2840, 2012.

Digital Library

Google Scholar

[79]

D. I. Shuman, B. Ricaud, and P. Vandergheynst. Vertex-frequency analysis on graphs. arXiv:1307.5708, 2013.

Google Scholar

[80]

N. Silberman, D. Hoiem, P. Kohli, and R. Fergus. Indoor segmentation and support inference from RGBD images. In Proc. ECCV, 2012.

Digital Library

Google Scholar

[81]

K. Simonyan and A. Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv:1409.1556, 2014.

Google Scholar

[82]

S. Song and J. Xiao. Deep sliding shapes for amodal 3D object detection in RGB-D images. arXiv:1511.02300, 2015.

Google Scholar

[83]

R. K. Srivastava, K. Greff, and J. Schmidhuber. Training very deep networks. In Proc. NIPS, 2015.

Digital Library

Google Scholar

[84]

R. K. Srivastava, J. Masci, F. J. Gomez, and J. Schmidhuber. Understanding locally competitive networks. In Proc. ICLR, 2015.

Google Scholar

[85]

M. Stollenga, J. Masci, F. Gomez, and J. Schmidhuber. Deep networks with internal selective attention through feedback connections. In Proc. NIPS, 2014.

Digital Library

Google Scholar

[86]

H. Su, S. Maji, E. Kalogerakis, and E. G. Learned-Miller. Multi-view convolutional neural networks for 3D shape recognition. In Proc. ICCV, 2015.

Digital Library

Google Scholar

[87]

J. Sun, M. Ovsjanikov, and L. J. Guibas. A concise and provably informative multi-scale signature based on heat diffusion. In Proc. SGP, 2009.

Digital Library

Google Scholar

[88]

J. Süssmuth, M. Winter, and G. Greiner. Reconstructing animated meshes from time-varying point clouds. Computer Graphics Forum, 27(5):1469--1476, 2008.

Digital Library

Google Scholar

[89]

C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich. Going deeper with convolutions. Technical report, 2014.

Google Scholar

[90]

J. Taylor, J. Shotton, T. Sharp, and A. Fitzgibbon. The vitruvian manifold: Inferring dense correspondences for one-shot human pose estimation. In Proc. CVPR, 2012.

Digital Library

Google Scholar

[91]

A. Tevs, A. Berner, M. Wand, I. Ihrke, M. Bokeloh, J. Kerber, and H.-P. Seidel. Animation cartography - intrinsic reconstruction of shape and motion. Trans. Graphics, 31(2):12:1--12:15, 2012.

Digital Library

Google Scholar

[92]

J. Tompson, M. Stein, Y. LeCun, and K. Perlin. Real-time continuous pose recovery of human hands using convolutional networks. Trans. Graphics, 33(5), 2014.

Digital Library

Google Scholar

[93]

P. Vincent, H. Larochelle, I. Lajoie, Y. Bengio, and P.-A. Manzagol. Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion. JMLR, 11:3371--3408, 2010.

Digital Library

Google Scholar

[94]

D. Vlasic, I. Baran, W. Matusik, and J. Popović. Articulated mesh animation from multi-view silhouettes. Trans. Graphics, 27(3), 2008.

Digital Library

Google Scholar

[95]

M. Wand, B. Adams, M. Ovsjanikov, A. Berner, M. Bokeloh, P. Jenke, L. Guibas, H.-P. Seidel, and A. Schilling. Efficient reconstruction of nonrigid shape and motion from real-time 3D scanner data. Trans. Graphics, 28(2), 2009.

Digital Library

Google Scholar

[96]

S. Wang and C. Manning. Fast dropout training. In Proc. ICML, 2013.

Google Scholar

[97]

L. Wei, Q. Huang, D. Ceylan, E. Vouga, and H. Li. Dense human body correspondences using convolutional networks. In Proc. CVPR, 2016.

Crossref

Google Scholar

[98]

X. Wei, P. Zhang, and J. Chai. Accurate realtime full-body motion capture using a single depth camera. Trans. Graphics, 31(6), 2012.

Digital Library

Google Scholar

[99]

P. J. Werbos. Beyond Regression: New Tools for Prediction and Analysis in the Behavioral Sciences. PhD thesis, Harvard University, 1974.

Google Scholar

[100]

Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao. 3D Shapenets: A deep representation for volumetric shapes. In Proc. CVPR, 2015.

Google Scholar

[101]

K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio. Show, attend and tell: Neural image caption generation with visual attention. In Proc. ICML, 2015.

Google Scholar

[102]

F. Yu and V. Koltun. Multi-scale context aggregation by dilated convolutions. In Proc. ICLR. 2016.

Google Scholar

[103]

M. E. Yumer and N. J. Mitra. Learning semantic deformation flows with 3d convolutional networks. In Proc. ECCV, 2016.

Crossref

Google Scholar

[104]

S. Zagoruyko and N. Komodakis. Learning to compare image patches via convolutional neural networks. In Proc. CVPR, 2015.

Crossref

Google Scholar

[105]

S. Zagoruyko, A. Lerer, T.-Y. Lin, P. O. Pinheiro, S. Gross, S. Chintala, and P. Dollár. A multipath network for object detection. In Proc. BMVC, 2016.

Crossref

Google Scholar

[106]

A. Zeng, S. Song, M. Nießner, M. Fisher, and J. Xiao. 3DMatch: Learning the matching of local 3D geometry in range scans. arXiv:1603.08182, 2016.

Google Scholar

[107]

S. Zhou, J. Wu, Y. Wu, and X. Zhou. Exploiting local structures with the kronecker layer in convolutional networks. arXiv:1512.09194, 2015.

Google Scholar

Cited By

View all

Barzegar Khalilsaraei SKomar AZheng JAugsdörfer U(2024)InceptCurves: curve reconstruction using an inception networkThe Visual Computer10.1007/s00371-024-03477-140:7(4805-4815)Online publication date: 12-Jun-2024
https://doi.org/10.1007/s00371-024-03477-1
Yan YZhou MZhang DGeng S(2024)Improved biharmonic kernel signature for 3D non-rigid shape matching and retrievalThe Visual Computer10.1007/s00371-023-03254-6Online publication date: 12-Feb-2024
https://doi.org/10.1007/s00371-023-03254-6
Bose KDas S(2023)Can Graph Neural Networks Go Deeper Without Over-Smoothing? Yes, With a Randomized Path Exploration!IEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32492557:5(1595-1604)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3249255
Show More Cited By

Index Terms

Geometric deep learning

Index terms have been assigned to the content through auto-classification.

Recommendations

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more
CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

Over the last decade, deep learning research has achieved tremendous success in computer vision and natural language processing. The current widely successful deep learning models are largely based on convolution and pooling operations on a Euclidean ...
Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning
Neural Information Processing
Abstract
As the two hottest branches of machine learning, deep learning and reinforcement learning both play a vital role in the field of artificial intelligence. Combining deep learning with reinforcement learning, deep reinforcement learning is a method ...

Comments

Information & Contributors

Information

Published In

SA '16: SIGGRAPH ASIA 2016 Courses

November 2016

1732 pages

ISBN:9781450345385

DOI:10.1145/2988458

Conference Chair:
Niloy J. Mitra
University College London, United Kingdom

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 28 November 2016

Check for updates

Qualifiers

Course

Conference

SA '16

Sponsor:

SIGGRAPH

SA '16: SIGGRAPH Asia 2016

December 5 - 8, 2016

Macau

Acceptance Rates

Overall Acceptance Rate 178 of 869 submissions, 20%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

25
Total Citations
View Citations
2,926
Total Downloads

Downloads (Last 12 months)94
Downloads (Last 6 weeks)2

Reflects downloads up to 03 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Barzegar Khalilsaraei SKomar AZheng JAugsdörfer U(2024)InceptCurves: curve reconstruction using an inception networkThe Visual Computer10.1007/s00371-024-03477-140:7(4805-4815)Online publication date: 12-Jun-2024
https://doi.org/10.1007/s00371-024-03477-1
Yan YZhou MZhang DGeng S(2024)Improved biharmonic kernel signature for 3D non-rigid shape matching and retrievalThe Visual Computer10.1007/s00371-023-03254-6Online publication date: 12-Feb-2024
https://doi.org/10.1007/s00371-023-03254-6
Bose KDas S(2023)Can Graph Neural Networks Go Deeper Without Over-Smoothing? Yes, With a Randomized Path Exploration!IEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2023.32492557:5(1595-1604)Online publication date: Oct-2023
https://doi.org/10.1109/TETCI.2023.3249255
Laurie MLu J(2023)Explainable deep learning for tumor dynamic modeling and overall survival prediction using Neural-ODEnpj Systems Biology and Applications10.1038/s41540-023-00317-19:1Online publication date: 18-Nov-2023
https://doi.org/10.1038/s41540-023-00317-1
Abidi KInoubli WNguifo E(2023)DGCN: Learning Graph Representations Via Dense ConnectionsProcedia Computer Science10.1016/j.procs.2023.10.082225(951-960)Online publication date: 2023
https://doi.org/10.1016/j.procs.2023.10.082
Brauer CLorenz DTondji L(2022)Group equivariant networks for leakage detection in vacuum bagging2022 30th European Signal Processing Conference (EUSIPCO)10.23919/EUSIPCO55093.2022.9909715(1437-1441)Online publication date: 29-Aug-2022
https://doi.org/10.23919/EUSIPCO55093.2022.9909715
Ullah MShagdar ZUllah HCheikh F(2022)Semi-Supervised Principal Neighbourhood Aggregation Model For Sar Image Classification2022 16th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS)10.1109/SITIS57111.2022.00046(211-217)Online publication date: Oct-2022
https://doi.org/10.1109/SITIS57111.2022.00046
Shagdar ZUllah MUllah HCheikh F(2021)Geometric Deep Learning for Multi-Object Tracking: A Brief Review2021 9th European Workshop on Visual Information Processing (EUVIP)10.1109/EUVIP50544.2021.9484040(1-6)Online publication date: 23-Jun-2021
https://doi.org/10.1109/EUVIP50544.2021.9484040
Dong SWang PAbbas K(2021)A survey on deep learning and its applicationsComputer Science Review10.1016/j.cosrev.2021.10037940:COnline publication date: 1-May-2021
https://dl.acm.org/doi/10.1016/j.cosrev.2021.100379
Marin RRampini ACastellani URodolà EOvsjanikov MMelzi S(2021)Spectral Shape Recovery and Analysis Via Data-driven ConnectionsInternational Journal of Computer Vision10.1007/s11263-021-01492-6129:10(2745-2760)Online publication date: 1-Oct-2021
https://dl.acm.org/doi/10.1007/s11263-021-01492-6
Show More Cited By

View Options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Cited By

Index Terms

Recommendations

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis

Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning

Comments

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Other Metrics

Article Metrics

Other Metrics

Cited By

Login options

Full Access

PDF

eReader

References

Cited By

Index Terms

Recommendations

Advanced Deep Learning with Keras: Apply deep learning techniques, autoencoders, GANs, variational autoencoders, deep reinforcement learning, policy gradients, and more

CurvaNet: Geometric Deep Learning based on Directional Curvature for 3D Shape Analysis

Deep Reinforcement Learning: From Q-Learning to Deep Q-Learning

Comments

Information

Published In

Sponsors

Publisher

Publication History

Check for updates

Qualifiers

Conference

Acceptance Rates

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

Get Access

Login options

Full Access

View options

PDF

eReader

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations