article{de2020introducing,
  title={Introducing routing uncertainty in capsule networks},
  author={De Sousa Ribeiro, Fabio and Leontidis, Georgios and Kollias, Stefanos},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={6490--6502},
  year={2020}
}

@inproceedings{de2020introducing,
 author = {De Sousa Ribeiro, Fabio and Leontidis, Georgios and Kollias, Stefanos},
 booktitle = {Advances in Neural Information Processing Systems},
 //editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {6490--6502},
 //publisher = {Curran Associates, Inc.},
 title = {Introducing Routing Uncertainty in Capsule Networks},
 //url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/47fd3c87f42f55d4b233417d49c34783-Paper.pdf},
 volume = {33},
 year = {2020}
}

inproceedings{deng2009imagenet,
  title={Imagenet: A large-scale hierarchical image database},
  author={Deng, Jia and Dong, Wei and Socher, Richard and Li, Li-Jia and Li, Kai and Fei-Fei, Li},
  booktitle={2009 IEEE Conference on Computer Vision and Pattern Recognition},
  pages={248--255},
  year={2009},
  organization={IEEE}
}

@article{peters2019sparse,
  title={Sparse sequence-to-sequence models},
  author={Peters, Ben and Niculae, Vlad and Martins, Andr{\'e} FT},
  journal={arXiv preprint arXiv:1905.05702},
  year={2019}
}

@article{chen2022fast,
  title={Fast robust capsule network with dynamic pruning and multiscale mutual information maximization for compound-fault diagnosis},
  author={Chen, Hao and Wang, Xian-bo and Yang, Zhi-Xin},
  journal={IEEE/ASME Transactions on Mechatronics},
  year={2022},
  publisher={IEEE}
}

@article{renzulli2022rem,
  title={REM: Routing entropy minimization for capsule networks},
  author={Renzulli, Riccardo and Tartaglione, Enzo and Grangetto, Marco},
  journal={arXiv preprint arXiv:2204.01298},
  year={2022}
}

@inproceedings{renzulli2022towards,
  title={Towards efficient capsule networks},
  author={Renzulli, Riccardo and Grangetto, Marco},
  booktitle={2022 IEEE International Conference on Image Processing (ICIP)},
  pages={2801--2805},
  year={2022},
  organization={IEEE}
}

inproceedings{jeong2019ladder,
  title={Ladder capsule network},
  author={Jeong, Taewon and Lee, Youngmin and Kim, Heeyoung},
  booktitle={International Conference on Machine Learning},
  pages={3071--3079},
  year={2019},
  organization={PMLR}
}
@InProceedings{jeong2019ladder,
  title = 	 {Ladder Capsule Network},
  author =       {Jeong, Taewon and Lee, Youngmin and Kim, Heeyoung},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {3071--3079},
  year = 	 {2019},
  //editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  //series = 	 {Proceedings of Machine Learning Research},
  //month = 	 {09--15 Jun},
  publisher =    {PMLR},
  //pdf = 	 {http://proceedings.mlr.press/v97/jeong19b/jeong19b.pdf},
  //url = 	 {https://proceedings.mlr.press/v97/jeong19b.html},
  //abstract = 	 {We propose a new architecture of the capsule network called the ladder capsule network, which has an alternative building block to the dynamic routing algorithm in the capsule network (Sabour et al., 2017). Motivated by the need for using only important capsules during training for robust performance, we first introduce a new layer called the pruning layer, which removes irrelevant capsules. Based on the selected capsules, we construct higher-level capsule outputs. Subsequently, to capture the part-whole spatial relationships, we introduce another new layer called the ladder layer, the outputs of which are regressed lower-level capsule outputs from higher-level capsules. Unlike the capsule network adopting the routing-by-agreement, the ladder capsule network uses backpropagation from a loss function to reconstruct the lower-level capsule outputs from higher-level capsules; thus, the ladder layer implements the reverse directional inference of the agreement/disagreement mechanism of the capsule network. The experiments on MNIST demonstrate that the ladder capsule network learns an equivariant representation and improves the capability to extrapolate or generalize to pose variations.}
}


@article{simonyan2014very,
  title={Very deep convolutional networks for large-scale image recognition},
  author={Simonyan, Karen and Zisserman, Andrew},
  journal={arXiv preprint arXiv:1409.1556},
  year={2014}
}

article{huang2020capsnet,
  title={DA-CapsNet: dual attention mechanism capsule network},
  author={Huang, Wenkai and Zhou, Fobao},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={11383},
  year={2020},
  publisher={Nature Publishing Group UK London}
}
@article{huang2020capsnet,
  title={DA-CapsNet: Dual attention mechanism capsule network},
  author={Huang, Wenkai and Zhou, Fobao},
  journal={Scientific Reports},
  volume={10},
  number={1},
  pages={11383},
  year={2020},
  publisher={Nature Publishing Group UK London}
}

@ARTICLE{9086631,
  author={Yang, Shuai and Lee, Feifei and Miao, Ran and Cai, Jiawei and Chen, Lu and Yao, Wei and Kotani, Koji and Chen, Qiu},
  journal={IEEE Access}, 
  title={RS-CapsNet: An Advanced Capsule Network}, 
  year={2020},
  volume={8},
  number={},
  pages={85007-85018},
  doi={10.1109/ACCESS.2020.2992655}}

inproceedings{guo2019simple,
  title={Simple black-box adversarial attacks},
  author={Guo, Chuan and Gardner, Jacob and You, Yurong and Wilson, Andrew Gordon and Weinberger, Kilian},
  booktitle={International Conference on Machine Learning},
  pages={2484--2493},
  year={2019},
  organization={PMLR}
}
@InProceedings{guo2019simple,
  title = 	 {Simple Black-box Adversarial Attacks},
  author =       {Guo, Chuan and Gardner, Jacob and You, Yurong and Wilson, Andrew Gordon and Weinberger, Kilian},
  booktitle = 	 {Proceedings of the 36th International Conference on Machine Learning},
  pages = 	 {2484--2493},
  year = 	 {2019},
  //editor = 	 {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
  volume = 	 {97},
  //series = 	 {Proceedings of Machine Learning Research},
  //month = 	 {09--15 Jun},
  publisher =    {PMLR},
  //pdf = 	 {http://proceedings.mlr.press/v97/guo19a/guo19a.pdf},
  //url = 	 {https://proceedings.mlr.press/v97/guo19a.html},
  //abstract = 	 {We propose an intriguingly simple method for the construction of adversarial images in the black-box setting. In constrast to the white-box scenario, constructing black-box adversarial images has the additional constraint on query budget, and efficient attacks remain an open problem to date. With only the mild assumption of requiring continuous-valued confidence scores, our highly query-efficient algorithm utilizes the following simple iterative principle: we randomly sample a vector from a predefined orthonormal basis and either add or subtract it to the target image. Despite its simplicity, the proposed method can be used for both untargeted and targeted attacks – resulting in previously unprecedented query efficiency in both settings. We demonstrate the efficacy and efficiency of our algorithm on several real world settings including the Google Cloud Vision API. We argue that our proposed algorithm should serve as a strong baseline for future black-box attacks, in particular because it is extremely fast and its implementation requires less than 20 lines of PyTorch code.}
}


@inproceedings{netzer2011reading,
  title={Reading digits in natural images with unsupervised feature learning},
  author={Netzer, Yuval and Wang, Tao and Coates, Adam and Bissacco, Alessandro and Wu, Bo and Ng, Andrew Y},
  booktitle={NIPS Workshop on Deep Learning and Unsupervised Feature Learning 2011},
  year={2011}
}

inproceedings{pucci2021self,
  title={Self-attention agreement among capsules},
  author={Pucci, Rita and Micheloni, Christian and Martinel, Niki},
  booktitle={Proceedings of the ieee/cvf international conference on computer vision},
  pages={272--280},
  year={2021}
}
@InProceedings{pucci2021self,
    author    = {Pucci, Rita and Micheloni, Christian and Martinel, Niki},
    title     = {Self-Attention Agreement Among Capsules},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
    //month     = {October},
    year      = {2021},
    pages     = {272-280}
}

@techreport{lecun2004learning,
  title={Learning methods for generic object recognition with invariance to pose and lighting},
  author={LeCun, Yann and Huang, Fu Jie and Bottou, L{\'e}on},
  year={2004},
  institution={Courant Institute of Mathematical Sciences, New York University}
}

@inproceedings{huang2017densely,
  title={Densely connected convolutional networks},
  author={Huang, Gao and Liu, Zhuang and Van Der Maaten, Laurens and Weinberger, Kilian Q},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={4700--4708},
  year={2017}
}


inproceedings{he2016deep,
  title={Deep residual learning for image recognition},
  author={He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
  booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
  pages={770--778},
  year={2016}
}
@InProceedings{he2016deep,
author = {He, Kaiming and Zhang, Xiangyu and Ren, Shaoqing and Sun, Jian},
title = {Deep Residual Learning for Image Recognition},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={770--778},
//month = {June},
year = {2016}
}

article{xiao2017fashion,
  title={Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms},
  author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
  journal={arXiv preprint arXiv:1708.07747},
  year={2017}
}
@article{xiao2017fashion,
  title={Fashion-{MNIST}: A novel image dataset for benchmarking machine learning algorithms},
  author={Xiao, Han and Rasul, Kashif and Vollgraf, Roland},
  journal={arXiv preprint arXiv:1708.07747},
  year={2017}
}

article{krizhevsky2009learning,
  title={Learning multiple layers of features from tiny images},
  author={Krizhevsky, Alex and Hinton, Geoffrey and others},
  year={2009},
  publisher={Toronto, ON, Canada}
}
@techreport{krizhevsky2009learning,
  title = {Learning multiple layers of features from tiny images},
  author = {Krizhevsky, Alex},
  year = {2009},
  institution = {University of Toronto},
  type = {Technical Report}
}

inproceedings{hinton2018matrix,
  title={Matrix capsules with EM routing},
  author={Hinton, Geoffrey E and Sabour, Sara and Frosst, Nicholas},
  booktitle={International conference on learning representations},
  year={2018}
}
@inproceedings{hinton2018matrix,
title={Matrix capsules with {EM} routing},
author={Geoffrey E Hinton and Sara Sabour and Nicholas Frosst},
booktitle={International Conference on Learning Representations},
year={2018},
//url={https://openreview.net/forum?id=HJWLfGWRb},
}

article{sabour2017dynamic,
  title={Dynamic routing between capsules},
  author={Sabour, Sara and Frosst, Nicholas and Hinton, Geoffrey E},
  journal={Advances in neural information processing systems},
  volume={30},
  year={2017}
}
@inproceedings{sabour2017dynamic,
 author = {Sabour, Sara and Frosst, Nicholas and Hinton, Geoffrey E},
 booktitle = {Advances in Neural Information Processing Systems},
 //editor = {I. Guyon and U. Von Luxburg and S. Bengio and H. Wallach and R. Fergus and S. Vishwanathan and R. Garnett},
 //pages = {},
 //publisher = {Curran Associates, Inc.},
 title = {Dynamic Routing Between Capsules},
 //url = {https://proceedings.neurips.cc/paper_files/paper/2017/file/2cad8fa47bbef282badbb8de5374b894-Paper.pdf},
 volume = {30},
 pages = {3856–3866},
 year = {2017}
}

@article{hahn2019self,
  title={Self-routing capsule networks},
  author={Hahn, Taeyoung and Pyeon, Myeongjang and Kim, Gunhee},
  journal={Advances in neural information processing systems},
  volume={32},
  year={2019}
}

@article{hoogi2019self,
  title={Self-attention capsule networks for object classification},
  author={Hoogi, Assaf and Wilcox, Brian and Gupta, Yachee and Rubin, Daniel L},
  journal={arXiv preprint arXiv:1904.12483},
  year={2019}
}

@article{tsai2020capsules,
  title={Capsules with inverted dot-product attention routing},
  author={Tsai, Yao-Hung Hubert and Srivastava, Nitish and Goh, Hanlin and Salakhutdinov, Ruslan},
  journal={arXiv preprint arXiv:2002.04764},
  year={2020}
}

article{mazzia2021efficient,
  title={Efficient-capsnet: Capsule network with self-attention routing},
  author={Mazzia, Vittorio and Salvetti, Francesco and Chiaberge, Marcello},
  journal={Scientific reports},
  volume={11},
  number={1},
  pages={14634},
  year={2021},
  publisher={Nature Publishing Group UK London}
}
@article{mazzia2021efficient,
  title={Efficient-capsnet: Capsule network with self-attention routing},
  author={Mazzia, Vittorio and Salvetti, Francesco and Chiaberge, Marcello},
  journal={Scientific Reports},
  volume={11},
  number={1},
  pages={14634},
  year={2021},
  publisher={Nature Publishing Group UK London}
}

@inproceedings{ribeiro2020capsule,
  title={Capsule routing via variational bayes},
  author={Ribeiro, Fabio De Sousa and Leontidis, Georgios and Kollias, Stefanos},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={34},
  number={04},
  pages={3749--3756},
  year={2020}
}

@article{li2020efficient,
  title={Efficient riemannian optimization on the stiefel manifold via the cayley transform},
  author={Li, Jun and Fuxin, Li and Todorovic, Sinisa},
  journal={arXiv preprint arXiv:2002.01113},
  year={2020}
}

@article{trockman2021orthogonalizing,
  title={Orthogonalizing convolutional layers with the cayley transform},
  author={Trockman, Asher and Kolter, J Zico},
  journal={arXiv preprint arXiv:2104.07167},
  year={2021}
}




inproceedings{singla2021skew,
  title={Skew orthogonal convolutions},
  author={Singla, Sahil and Feizi, Soheil},
  booktitle={International Conference on Machine Learning},
  pages={9756--9766},
  year={2021},
  organization={PMLR}
}
@InProceedings{singla2021skew,
  title = 	 {Skew Orthogonal Convolutions},
  author =       {Singla, Sahil and Feizi, Soheil},
  booktitle = 	 {Proceedings of the 38th International Conference on Machine Learning},
  pages = 	 {9756--9766},
  year = 	 {2021},
  //editor = 	 {Meila, Marina and Zhang, Tong},
  volume = 	 {139},
  //series = 	 {Proceedings of Machine Learning Research},
  //month = 	 {18--24 Jul},
  publisher =    {PMLR},
  //pdf = 	 {http://proceedings.mlr.press/v139/singla21a/singla21a.pdf},
  //url = 	 {https://proceedings.mlr.press/v139/singla21a.html},
  //abstract = 	 {Training convolutional neural networks with a Lipschitz constraint under the $l_{2}$ norm is useful for provable adversarial robustness, interpretable gradients, stable training, etc. While 1-Lipschitz networks can be designed by imposing a 1-Lipschitz constraint on each layer, training such networks requires each layer to be gradient norm preserving (GNP) to prevent gradients from vanishing. However, existing GNP convolutions suffer from slow training, lead to significant reduction in accuracy and provide no guarantees on their approximations. In this work, we propose a GNP convolution layer called \textbf{S}kew \textbf{O}rthogonal \textbf{C}onvolution (SOC) that uses the following mathematical property: when a matrix is {\it Skew-Symmetric}, its exponential function is an {\it orthogonal} matrix. To use this property, we first construct a convolution filter whose Jacobian is Skew-Symmetric. Then, we use the Taylor series expansion of the Jacobian exponential to construct the SOC layer that is orthogonal. To efficiently implement SOC, we keep a finite number of terms from the Taylor series and provide a provable guarantee on the approximation error. Our experiments on CIFAR-10 and CIFAR-100 show that SOC allows us to train provably Lipschitz, large convolutional neural networks significantly faster than prior works while achieving significant improvements for both standard and certified robust accuracies.}
}


inproceedings{huang2018orthogonal,
  title={Orthogonal weight normalization: Solution to optimization over multiple dependent stiefel manifolds in deep neural networks},
  author={Huang, Lei and Liu, Xianglong and Lang, Bo and Yu, Adams and Wang, Yongliang and Li, Bo},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  volume={32},
  number={1},
  year={2018}
}
@article{huang2018orthogonal, 
  title={Orthogonal Weight Normalization: Solution to Optimization Over Multiple Dependent Stiefel Manifolds in Deep Neural Networks}, 
  volume={32}, 
  //url={https://ojs.aaai.org/index.php/AAAI/article/view/11768}, 
  //DOI={10.1609/aaai.v32i1.11768}, 
  //abstractNote={ &lt;p&gt; Orthogonal matrix has shown advantages in training Recurrent Neural Networks (RNNs), but such matrix is limited to be square for the hidden-to-hidden transformation in RNNs. In this paper, we generalize such square orthogonal matrix to orthogonal rectangular matrix and formulating this problem in feed-forward Neural Networks (FNNs) as Optimization over Multiple Dependent Stiefel Manifolds (OMDSM). We show that the orthogonal rectangular matrix can stabilize the distribution of network activations and regularize FNNs. We propose a novel orthogonal weight normalization method to solve OMDSM. Particularly, it constructs orthogonal transformation over proxy parameters to ensure the weight matrix is orthogonal. To guarantee stability, we minimize the distortions between proxy parameters and canonical weights over all tractable orthogonal transformations. In addition, we design orthogonal linear module (OLM) to learn orthogonal filter banks in practice, which can be used as an alternative to standard linear module. Extensive experiments demonstrate that by simply substituting OLM for standard linear module without revising any experimental protocols, our method improves the performance of the state-of-the-art networks, including Inception and residual networks on CIFAR and ImageNet datasets. &lt;/p&gt; }, 
  number={1}, 
  journal={Proceedings of the AAAI Conference on Artificial Intelligence}, 
  author={Huang, Lei and Liu, Xianglong and Lang, Bo and Yu, Adams and Wang, Yongliang and Li, Bo}, 
  year={2018}, 
  //month={Apr.} 
}

article{virmaux2018lipschitz,
  title={Lipschitz regularity of deep neural networks: analysis and efficient estimation},
  author={Virmaux, Aladin and Scaman, Kevin},
  journal={Advances in Neural Information Processing Systems},
  volume={31},
  year={2018}
}
@inproceedings{virmaux2018lipschitz,
 author = {Virmaux, Aladin and Scaman, Kevin},
 booktitle = {Advances in Neural Information Processing Systems},
 //editor = {S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett},
 pages = {3835-3844},
 //publisher = {Curran Associates, Inc.},
 title = {Lipschitz regularity of deep neural networks: Analysis and efficient estimation},
 //url = {https://proceedings.neurips.cc/paper_files/paper/2018/file/d54e99a6c03704e95e6965532dec148b-Paper.pdf},
 //volume = {31},
 year = {2018}
}


inproceedings{qi2020deep,
  title={Deep isometric learning for visual recognition},
  author={Qi, Haozhi and You, Chong and Wang, Xiaolong and Ma, Yi and Malik, Jitendra},
  booktitle={International conference on machine learning},
  pages={7824--7835},
  year={2020},
  organization={PMLR}
}
@InProceedings{qi2020deep,
  title = 	 {Deep Isometric Learning for Visual Recognition},
  author =       {Qi, Haozhi and You, Chong and Wang, Xiaolong and Ma, Yi and Malik, Jitendra},
  booktitle = 	 {Proceedings of the 37th International Conference on Machine Learning},
  pages = 	 {7824--7835},
  year = 	 {2020},
  //editor = 	 {III, Hal Daumé and Singh, Aarti},
  volume = 	 {119},
  //series = 	 {Proceedings of Machine Learning Research},
  //month = 	 {13--18 Jul},
  publisher =    {PMLR},
  //pdf = 	 {http://proceedings.mlr.press/v119/qi20a/qi20a.pdf},
  //url = 	 {https://proceedings.mlr.press/v119/qi20a.html},
  //abstract = 	 {Initialization, normalization, and skip connections are believed to be three indispensable techniques for training very deep convolutional neural networks and obtaining state-of-the-art performance. This paper shows that deep vanilla ConvNets without normalization nor skip connections can also be trained to achieve surprisingly good performance on standard image recognition benchmarks. This is achieved by enforcing the convolution kernels to be near isometric during initialization and training, as well as by using a variant of ReLU that is shifted towards being isometric. Further experiments show that if combined with skip connections, such near isometric networks can achieve performances on par with (for ImageNet) and better than (for COCO) the standard ResNet, even without normalization at all. Our code is available at https://github.com/HaozhiQi/ISONet.}
}

@inproceedings{sharifi2021prunedcaps,
  title={Prunedcaps: A case for primary capsules discrimination},
  author={Sharifi, Ramin and Shiri, Pouya and Baniasadi, Amirali},
  booktitle={2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA)},
  pages={1437--1442},
  year={2021},
  organization={IEEE}
}

inproceedings{huang2020controllable,
  title={Controllable orthogonalization in training dnns},
  author={Huang, Lei and Liu, Li and Zhu, Fan and Wan, Diwen and Yuan, Zehuan and Li, Bo and Shao, Ling},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6429--6438},
  year={2020}
}
@inproceedings{huang2020controllable,
  title={Controllable orthogonalization in training {DNNs}},
  author={Huang, Lei and Liu, Li and Zhu, Fan and Wan, Diwen and Yuan, Zehuan and Li, Bo and Shao, Ling},
  booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
  pages={6429--6438},
  year={2020}
}

@article{peng2020bg,
  title={BG-SAC: Entity relationship classification model based on Self-Attention supported Capsule Networks},
  author={Peng, Dunlu and Zhang, Dongdong and Liu, Cong and Lu, Jing},
  journal={Applied Soft Computing},
  volume={91},
  pages={106186},
  year={2020},
  publisher={Elsevier}
}

inproceedings{choi2019attention,
  title={Attention routing between capsules},
  author={Choi, Jaewoong and Seo, Hyun and Im, Suii and Kang, Myungjoo},
  booktitle={Proceedings of the IEEE/CVF international conference on computer vision workshops},
  pages={0--0},
  year={2019}
}
@InProceedings{choi2019attention,
author = {Choi, Jaewoong and Seo, Hyun and Im, Suii and Kang, Myungjoo},
title = {Attention Routing Between Capsules},
booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops},
//month = {Oct},
year = {2019}
}

@article{ba2016layer,
  title={Layer normalization},
  author={Ba, Jimmy Lei and Kiros, Jamie Ryan and Hinton, Geoffrey E},
  journal={arXiv preprint arXiv:1607.06450},
  year={2016}
}

article{mathiasen2020if,
  title={What if neural networks had SVDs?},
  author={Mathiasen, Alexander and Hvilsh{\o}j, Frederik and R{\o}dsgaard J{\o}rgensen, Jakob and Nasery, Anshul and Mottin, Davide},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18411--18420},
  year={2020}
}
article{mathiasen2020if,
  title={What if neural networks had {SVD}s?},
  author={Mathiasen, Alexander and Hvilsh{\o}j, Frederik and R{\o}dsgaard J{\o}rgensen, Jakob and Nasery, Anshul and Mottin, Davide},
  journal={Advances in Neural Information Processing Systems},
  volume={33},
  pages={18411--18420},
  year={2020}
}
@inproceedings{mathiasen2020if,
 author = {Mathiasen, Alexander and Hvilsh\o j, Frederik and R\o dsgaard J\o rgensen, Jakob and Nasery, Anshul and Mottin, Davide},
 booktitle = {Advances in Neural Information Processing Systems},
 //editor = {H. Larochelle and M. Ranzato and R. Hadsell and M.F. Balcan and H. Lin},
 pages = {18411--18420},
 //publisher = {Curran Associates, Inc.},
 title = {What if Neural Networks had {SVD}s?},
 //url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/d61e4bbd6393c9111e6526ea173a7c8b-Paper.pdf},
 volume = {33},
 year = {2020}
}


@inproceedings{gagana2018activation,
  title={Activation function optimizations for capsule networks},
  author={Gagana, B and Athri, HA Ujjwal and Natarajan, S},
  booktitle={2018 International Conference on Advances in Computing, Communications and Informatics (ICACCI)},
  pages={1172--1178},
  year={2018},
  organization={IEEE}
}

inproceedings{rajasegaran2019deepcaps,
  title={Deepcaps: Going deeper with capsule networks},
  author={Rajasegaran, Jathushan and Jayasundara, Vinoj and Jayasekara, Sandaru and Jayasekara, Hirunima and Seneviratne, Suranga and Rodrigo, Ranga},
  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition},
  pages={10725--10733},
  year={2019}
}
@InProceedings{rajasegaran2019deepcaps,
author = {Rajasegaran, Jathushan and Jayasundara, Vinoj and Jayasekara, Sandaru and Jayasekara, Hirunima and Seneviratne, Suranga and Rodrigo, Ranga},
title = {DeepCaps: Going Deeper With Capsule Networks},
booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={10725--10733},
//month = {June},
year = {2019}
}

@article{kingma2014adam,
  title={Adam: A method for stochastic optimization},
  author={Kingma, Diederik P and Ba, Jimmy},
  journal={arXiv preprint arXiv:1412.6980},
  year={2014}
}


article{lecun1998mnist,
  title={The MNIST database of handwritten digits},
  author={LeCun, Yann},
  journal={http://yann. lecun. com/exdb/mnist/},
  year={1998}
}
@article{lecun1998mnist,
  title={The {MNIST} database of handwritten digits},
  author={LeCun, Yann},
  journal={http://yann. lecun. com/exdb/mnist/},
  year={1998}
}

@article{rodriguez2016regularizing,
  title={Regularizing cnns with locally constrained decorrelations},
  author={Rodr{\'\i}guez, Pau and Gonzalez, Jordi and Cucurull, Guillem and Gonfaus, Josep M and Roca, Xavier},
  journal={arXiv preprint arXiv:1611.01967},
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