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We propose a new model for relational VAE semi-supervision capable of balancing disentanglement and low complexity modelling of relations with different symbolic properties.
J. C. Raven, “Standardization of progressive matrices, 1938,” British Journal of Medical Psychology , vol. 19, no. 1, pp. 137–150, 1941
1941
Earlier work this paper cites.
C. Kemp and J. B. Tenenbaum, “The discovery of structural form,” Proceedings of the National Academy of Sciences of the United States of America , vol. 105, no. 31, pp. 10 687–10 692, 2008
2008
Earlier work this paper cites.
S. J. Pan and Q. Yang, “A survey on transfer learning,” IEEE Trans. on Knowl. and Data Eng. , vol. 22, no. 10, p. 1345–1359, Oct. 2010
2010
Earlier work this paper cites.
Y. LeCun and C. Cortes, “MNIST handwritten digit database,” 2010. [Online]. Available: http://yann.lecun.com/exdb/mnist/
2010
Earlier work this paper cites.
Y. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 35, no. 8, pp. 1798–1828, 2013
2013
Earlier work this paper cites.
R. Socher, D. Chen, C. Manning, D. Chen, and A. Ng, “Reasoning With Neural Tensor Networks for Knowledge Base Completion,” in Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems , 2013, pp. 926–934
2013
Earlier work this paper cites.
D. P. Kingma, S. Mohamed, D. J. Rezende, and M. Welling, “Semi-supervised Learning with Deep Generative Models,” in Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems , Montreal, Quebec, 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 , Banff, Alberta, Canada, 2014
2014
Earlier work this paper cites.
T. Karaletsos, S. Belongie, and G. Rätsch, “When crowds hold privileges: Bayesian unsupervised representation learning with oracle constraints,” in 4th International Conference on Learning Representations, {
2016
Earlier work this paper cites.
M. Nickel, K. Murphy, V. Tresp, and E. Gabrilovich, “A review of relational machine learning for knowledge graphs,” Proceedings of the IEEE , vol. 104, no. 1, pp. 11–33, 2016
2016
Earlier work this paper cites.
L. Serafini and A. D. Garcez, “Logic tensor networks: Deep learning and logical reasoning from data and knowledge,” in Proceedings of the 11th International Workshop on Neural-Symbolic Learning and Reasoning (NeSy’16) co-located with the Joint Multi-Conference on Human-Level Artificial Intelligence {
2016
Earlier work this paper cites.
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner, “beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework,” in 5th International Conference on Learning Representations, {
2017
Earlier work this paper cites.
Q. Wang, Z. Mao, B. Wang, and L. Guo, “Knowledge graph embedding: A survey of approaches and applications,” IEEE Transactions on Knowledge and Data Engineering , vol. 29, no. 12, pp. 2724—-2743, 2017
2017
Cited alongside, same era.
I. Donadello, L. Serafini, and A. d’Avila Garcez, “Logic Tensor Networks for Semantic Image Interpretation,” in Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence , 2017, pp. 1596—-1602
2017
Cited alongside, same era.
2017
Cited alongside, same era.
A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap, “A simple neural network module for relational reasoning,” in Advances in Neural Information Processing Systems 30 , Long Beach, CA, USA, 2017
V. Gutiérrez-Basulto and S. Schockaert, “From Knowledge Graph Embedding to Ontology Embedding? An Analysis of the Compatibility between Vector Space Representations and Rules,” in Principles of Knowledge Representation and Reasoning: Proceedings of the Sixteenth International Conference , Tempe, Arizona, US, 2018
2018
Later among the works it cites.
A. Trask, F. Hill, S. E. Reed, J. W. Rae, C. Dyer, and P. Blunsom, “Neural arithmetic logic units,” in Advances in Neural Information Processing Systems 31 , Montreal, Canada, 2018, pp. 8046–8055
2018
Later among the works it cites.
F. Locatello, S. Bauer, M. Lucic, G. Rätsch, S. Gelly, B. Schölkopf, and O. Bachem, “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations,” in Proceedings of the 36th International Conference on Machine Learning, {
2019
Later among the works it cites.
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2017
Cited alongside, same era.
Z. Feng, A. Zeng, X. Wang, D. Tao, C. Ke, and M. Song, “Dual swap disentangling,” in Advances in Neural Information Processing Systems 32 , Montreal, Canada, 2018, pp. 5894–5904
2018
Cited alongside, same era.
R. T. Q. Chen, X. Li, R. B. Grosse, and D. Duvenaud, “Isolating Sources of Disentanglement in Variational Autoencoders,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems , Montreal, Quebec, Canada, 2018, pp. 2615—-2625
2018
Cited alongside, same era.
A. Kumar, P. Sattigeri, and A. Balakrishnan, “Variational inference of disentangled latent concepts from unlabeled observations,” in 6th International Conference on Learning Representations, {
2018
Cited alongside, same era.
H. Kim and A. Mnih, “Disentangling by Factorising,” in Proceedings of the 35th International Conference on Machine Learning, {
2018
Cited alongside, same era.
K. Ridgeway and M. C. Mozer, “Learning Deep Disentangled Embeddings With the F-Statistic Loss,” in Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems , Montreal, Quebec, Canada, 2018, pp. 185—-194
2018
Cited alongside, same era.
C. Eastwood and C. K. I. Williams, “A framework for the quantitative evaluation of disentangled representations,” in 6th International Conference on Learning Representations, {
2018
Cited alongside, same era.
X. Steenbrugge, S. Leroux, T. Verbelen, and B. Dhoedt, “Improving Generalization for Abstract Reasoning Tasks Using Disentangled Feature Representations,” in Neural Information Processing Systems (NeurIPS) Workshop on Relational Representation Learning , Montreal, Canada, 2018
2018
Cited alongside, same era.
D. G. Barrett, F. Hill, A. Santoro, A. S. Morcos, and T. Lillicrap, “Measuring abstract reasoning in neural networks,” 35th International Conference on Machine Learning, ICML 2018 , vol. 10, pp. 7118–7127, 2018
2018
Cited alongside, same era.
2019
Later among the works it cites.
T. Trouillon, É. Gaussier, C. R. Dance, and G. Bouchard, “On inductive abilities of latent factor models for relational learning,” Journal of Artificial Intelligence Research , vol. 64, pp. 21–53, 2019
2019
Later among the works it cites.
S. van Steenkiste, F. Locatello, J. Schmidhuber, and O. Bachem, “Are Disentangled Representations Helpful for Abstract Visual Reasoning?” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems , Vancouver, BC, Canada, 2019, pp. 14 222—-14 235
2019
Later among the works it cites.
A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds. Curran Associates, Inc., 2019, pp. 8024–8035. [Online]. Available: http://papers.neurips.cc/paper/9015-pytorch-an-imperative-style-high-performance-deep-learning-library.pdf
2019
Later among the works it cites.
R. Shu, Y. Chen, A. Kumar, S. Ermon, and B. Poole, “Weakly Supervised Disentanglement With Guarantees,” in 8th International Conference on Learning Representations, ICLR , Addis Ababa, Ethiopia, 2020
2020
Closest in time.
F. Locatello, B. Poole, G. Rätsch, B. Schölkopf, O. Bachem, and M. Tschannen, “Weakly-Supervised Disentanglement Without Compromises,” CoRR , vol. abs/2002.0, 2020
2020
Closest in time.
J. Chen and K. Batmanghelich, “Weakly Supervised Disentanglement by Pairwise Similarities,” in Proceedings of the 32nd AAAI Conference on Artificial Intelligence, AAAI , New York, NY, USA, 2020
2020
Closest in time.
A. Madsen and A. R. Johansen, “Neural Arithmetic Units,” in 8th International Conference on Learning Representations, ICLR , Addis Ababa, Ethiopia, 2020
2020
Closest in time.