Fetching the paper…
Reading the bibliography…
Deep generative models are tremendously successful in learning low-dimensional latent representations that well-describe the data.
Z. Jiang, Y. Zheng, H. Tan, B. Tang, and H. Zhou, “Variational deep embedding: an unsupervised and generative approach to clustering,” in Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI) , Melbourne, Australia, 2017, pp. 1965–1972
1972
Earlier work this paper cites.
L. B. Rall, “Automatic differentiation: Techniques and applications,” 1981
1981
Earlier work this paper cites.
L. Kaufman and P. Rousseeuw, Clustering by means of medoids . North-Holland, 1987
1987
Earlier work this paper cites.
S. Gallot, D. Hulin, and J. Lafontaine, Riemannian geometry . Springer, 1990, vol. 3
1990
Earlier work this paper cites.
C. M. Bishop, M. Svensen, and C. K. Williams, “Magnification factors for the gtm algorithm,” 1997
1997
Earlier work this paper cites.
C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics) . Secaucus, NJ, USA: Springer-Verlag New York, Inc., 2006
2006
Earlier work this paper cites.
U. V. Luxburg, “A tutorial on spectral clustering,” Statistics and Computing , vol. 17, pp. 395–416, 2007
2007
Earlier work this paper cites.
O. Cappé, S. J. Godsill, and E. Moulines, “An overview of existing methods and recent advances in sequential monte carlo,” Proceedings of the IEEE , vol. 95, no. 5, pp. 899–924, 2007
2007
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Proceedings of the 26th Conference on Neural Information Processing Systems (NIPS) , Harrahs and Harveys, Lake Tahoe, 2012
2012
Earlier work this paper cites.
D. P. Kingma, D. J. Rezende, S. Mohamed, and M. Welling, “Semi-supervised learning with deep generative models,” in Proceedings of the 28th Neural Information Processing Systems (NIPS) , Montréal Canada, 2014, pp. 3581–3589
2014
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” in Proceedings of the 2nd International Conference on Learning Representations (ICLR) , Banff, Canada, 2014
2014
Earlier work this paper cites.
D. J. Rezende, S. Mohamed, and D. Wiestra, “Stochastic backpropagation and approximate inference in deep generative models,” in Proceedings of the 31st International Conference on Machine Learning (ICML) , Beijing, China, 2014
2014
Earlier work this paper cites.
P. Huang, Y. Huang, W. Wang, and L. Wang, “Deep embedding network for clustering,” in Proceedings of the 22nd International Conference on Pattern Recognition , Stockholm, Sweden, 2014, pp. 1532–1537
2014
Cited alongside, same era.
R. Sarikaya, G. E. Hinton, and A. Deoras, “Application of deep belief networks for natural language understanding,” ACM Transactions on Audio Speech & Language Processing , vol. 22, pp. 778–784, 2014
2014
Cited alongside, same era.
P. Hennig and S. Hauberg, “Probabilistic solutions to differential equations and their application to riemannian statistics,” in Proceedings of the 17th international Conference on Artificial Intelligence and Statistics (AISTATS) , vol. 33, 2014
2014
Cited alongside, same era.
A. Tosi, S. Hauberg, A. Vellido, and N. D. Lawrence, “Metrics for Probabilistic Geometries,” in The Conference on Uncertainty in Artificial Intelligence (UAI) , Jul. 2014
2014
Cited alongside, same era.
D. Chen, J. Lv, and Z. Yi, “Unsupervised multi-manifold clustering by learning deep representation,” in Proceedings of the 31st AAAI Conference on Artificial Intelligence , San Francisco, California USA, 2017, pp. 385–391
2017
Later among the works it cites.
K. G. Dizaji, A. Herandi, C. Deng, W. Cai, and H. Huang, “Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization,” in Proceedings of the IEEE International Conference on Computer Vision (ICCV) , Venice, Italy, 2017, pp. 5736–5745
2017
Later among the works it cites.
——, “Robust continuous clustering,” Proceedings of the National Academy of Sciences of the United States of America , vol. 114, pp. 9814–9819, 2017
2017
Later among the works it cites.
J. Chang, L. Wang, G. Meng, S. Xiang, and C. Pan, “Deep adaptive image clustering,” in Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Honolulu, Hawaii, 2017, pp. 5879–5887
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
E. Denton, S. Chintala, A. Szlam, and R. Fergus, “Deep generative image models using a laplacian pyramid of adversarial networks,” in Proceedings of the 29th Neural Information Processing Systems (NIPS) , Montréal Canada, 2015, pp. 1486–1494
2015
Cited alongside, same era.
G. Chen, “Deep learning with nonparametric clustering,” arXiv:1501.03084 , 2015
2015
Cited alongside, same era.
J. Xie, R. Girshick, and A. Farhadi, “Unsupervised deep embedding for clustering analysis,” in Proceedings of the 33rd International Conference on Machine Learning (ICML) , New York, 2016, pp. 478–487
2016
Cited alongside, same era.
J. Yang, D. Parikh, and D. Batra, “Joint unsupervised learning of deep representations and image clusters,” in Proceedings of the 29th IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , Las Vegas, 2016, pp. 5147–5156
2016
Cited alongside, same era.
2016
Cited alongside, same era.
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel, “Infogan: Interpretable representation learning by information maximizing generative adversarial nets,” in Proceedings of the 5th Proceedings of the 30th Neural Information Processing Systems (NIPS) , Barcelona, Spain, 2016, pp. 2172–2180
2016
Cited alongside, same era.
Q. Que and M. Belkin, “Back to the future: Radial basis function networks revisited,” in Artificial Intelligence and Statistics (AISTATS) , 2016
2016
Cited alongside, same era.
B. Yang, X. Xiao, N. Sidiropoulos, and M. Hong, “Towards k-means-friendly spaces: Simultaneous deep learning and clustering,” in Proceedings of the 34th International Conference on Machine Learning (ICML) , Sydney, Australia, 2017, pp. 3861–3870
2017
Cited alongside, same era.
W. Harchaoui, P. A. Mattei, and C. Bouveyron, “Deep adversarial gaussian mixture auto-encoder for clustering,” in Workshop of the 5th International Conference on Learning Representations (ICLR) , Toulon, France, 2017
2017
Later among the works it cites.
H. Xiao, K. Rasul, and R. Vollgraf. (2017) Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms
2017
Later among the works it cites.
2017
Later among the works it cites.
G. Arvanitidis, L. Hansen, and S. Hauberg, “Latent space oddity: on the curvature of deep generative models,” in Proceedings of the 6th International Conference on Learning Representations (ICLR) , Vancouver, Canada, 2018
2018
Closest in time.
S. A. Shah and V. Koltun, “Deep continuous clustering,” arXiv:1803.01449 , 2018
2018
Closest in time.
C. C. Hsu and C. W. Lin, “Cnn-based joint clustering and representation learning with feature drift compensation for large-scale image data,” IEEE Transactions on Multimedia , vol. 20, pp. 421 – 429, 2018
2018
Closest in time.
S. Hauberg, “Only bayes should learn a manifold,” 2018
2018
Closest in time.
S. Laine, “Feature-based metrics for exploring the latent space of generative models,” ICLR workshops , 2018
2018
Closest in time.