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S. Watanabe, “Information theoretical analysis of multivariate correlation,” IBM Journal of research and development
1960
Earlier work this paper cites.
G. E. Hinton and R. S. Zemel, “Autoencoders, minimum description length, and helmholtz free energy,” NeurIPS
1994
Earlier work this paper cites.
J. Liu and et al, “uwave: Accelerometer-based personalized gesture recognition and its applications,” Pervasive and Mobile Computing
2009
Earlier work this paper cites.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SigKDD Explorations Newsletter
2011
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
Original
2013
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A public domain dataset for human activity recognition using smartphones.,” in Esann
2013
Earlier work this paper cites.
E. Tzeng and et al, “Deep domain confusion: Maximizing for domain invariance,” arXiv preprint arXiv:1412.3474
Original
2014
Earlier work this paper cites.
H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand, “Domain-adversarial neural networks,” arXiv preprint arXiv:1412.4446
Original
2014
Earlier work this paper cites.
S. Ben-David and R. Urner, “Domain adaptation–can quantity compensate for quality?,” Annals of Mathematics and Artificial Intelligence
Original
2014
Earlier work this paper cites.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” arXiv preprint arXiv:1506.02216
Original
2015
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in ICML
2015
Earlier work this paper cites.
N. Tishby and N. Zaslavsky, “Deep learning and the information bottleneck principle,” in 2015 IEEE Information Theory Workshop (ITW)
2015
Earlier work this paper cites.
A. Stisen and et al, “Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition,” in SenSys
2015
Earlier work this paper cites.
X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel, “Variational lossy autoencoder,” arXiv:1611.02731
Original
2016
Earlier work this paper cites.

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Then

R. Cai, Z. Li, P. Wei, J. Qiao, K. Zhang, and Z. Hao, “Learning disentangled semantic representation for domain adaptation,” in IJCAI
2019
Later among the works it cites.
F. Locatello and et al, “Challenging common assumptions in the unsupervised learning of disentangled representations,” in ICML
2019
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Y. Shen, C. Yang, X. Tang, and B. Zhou, “Interfacegan: Interpreting the disentangled face representation learned by gans,” TPAMI
2020
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K.-H. Lai, D. Zha, G. Wang, J. Xu, Y. Zhao, D. Kumar, Y. Chen, P. Zumkhawaka, M. Wan, D. Martinez, et al
Original
2020
Later among the works it cites.
D. Zha, K.-H. Lai, M. Wan, and X. Hu, “Meta-aad: Active anomaly detection with deep reinforcement learning,” arXiv preprint arXiv:2009.07415
Original

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2021

Learning Disentangled Representations for Time Series

Li, Yuening, Chen, Zhengzhang, Zha, Daochen et al.

Understand

Time-series representation learning is a fundamental task for time-series analysis.

While significant progress has been made to achieve accurate representations for downstream applications, the learned representations often lack interpretability and do not expose semantic meanings.
Different from previous efforts on the entangled feature space, we aim to extract the semantic-rich temporal correlations in the latent interpretable factorized representation of the data.
Motivated by the success of disentangled representation learning in computer vision, we study the possibility of learning semantic-rich time-series representations, which remains unexplored due to three main challenges: 1) sequential data structure introduces complex temporal correlations and makes the latent representations hard to interpret, 2) sequential models suffer from KL vanishing problem, and 3) interpretable semantic concepts for time-series often rely on multiple factors instead of individuals.

Built on

S. Watanabe, “Information theoretical analysis of multivariate correlation,” IBM Journal of research and development
1960
Earlier work this paper cites.
G. E. Hinton and R. S. Zemel, “Autoencoders, minimum description length, and helmholtz free energy,” NeurIPS
1994
Earlier work this paper cites.
J. Liu and et al, “uwave: Accelerometer-based personalized gesture recognition and its applications,” Pervasive and Mobile Computing
2009
Earlier work this paper cites.
J. R. Kwapisz, G. M. Weiss, and S. A. Moore, “Activity recognition using cell phone accelerometers,” ACM SigKDD Explorations Newsletter
2011
Earlier work this paper cites.
D. P. Kingma and M. Welling, “Auto-encoding variational bayes,” arXiv preprint arXiv:1312.6114
Original
2013
Earlier work this paper cites.
D. Anguita, A. Ghio, L. Oneto, X. Parra, and J. L. Reyes-Ortiz, “A public domain dataset for human activity recognition using smartphones.,” in Esann
2013
Earlier work this paper cites.
E. Tzeng and et al, “Deep domain confusion: Maximizing for domain invariance,” arXiv preprint arXiv:1412.3474
Original
2014
Earlier work this paper cites.
H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, and M. Marchand, “Domain-adversarial neural networks,” arXiv preprint arXiv:1412.4446
Original
2014
Earlier work this paper cites.
S. Ben-David and R. Urner, “Domain adaptation–can quantity compensate for quality?,” Annals of Mathematics and Artificial Intelligence
Original
2014
Earlier work this paper cites.
J. Chung, K. Kastner, L. Dinh, K. Goel, A. Courville, and Y. Bengio, “A recurrent latent variable model for sequential data,” arXiv preprint arXiv:1506.02216
Original
2015
Earlier work this paper cites.
Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation,” in ICML
2015
Earlier work this paper cites.
N. Tishby and N. Zaslavsky, “Deep learning and the information bottleneck principle,” in 2015 IEEE Information Theory Workshop (ITW)
2015
Earlier work this paper cites.
A. Stisen and et al, “Smart devices are different: Assessing and mitigatingmobile sensing heterogeneities for activity recognition,” in SenSys
2015
Earlier work this paper cites.
X. Chen, D. P. Kingma, T. Salimans, Y. Duan, P. Dhariwal, J. Schulman, I. Sutskever, and P. Abbeel, “Variational lossy autoencoder,” arXiv:1611.02731
Original
2016
Earlier work this paper cites.

Similar

S. Semeniuta and et al, “A hybrid convolutional variational autoencoder for text generation,” arXiv preprint arXiv:1702.02390
Original
2017
Cited alongside, same era.
S. Purushotham, W. Carvalho, T. Nilanon, and Y. Liu, “Variational recurrent adversarial deep domain adaptation.,” in ICLR
2017
Cited alongside, same era.
V. Fortuin and et al, “Som-vae: Interpretable discrete representation learning on time series,” arXiv preprint arXiv:1806.02199
Original
2018
Cited alongside, same era.
R. T. Chen, X. Li, R. Grosse, and D. Duvenaud, “Isolating sources of disentanglement in variational autoencoders,” arXiv preprint arXiv:1802.04942
Original
2018
Cited alongside, same era.
C. P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, et al
Original

Then

R. Cai, Z. Li, P. Wei, J. Qiao, K. Zhang, and Z. Hao, “Learning disentangled semantic representation for domain adaptation,” in IJCAI
2019
Later among the works it cites.
F. Locatello and et al, “Challenging common assumptions in the unsupervised learning of disentangled representations,” in ICML
2019
Later among the works it cites.
Y. Shen, C. Yang, X. Tang, and B. Zhou, “Interfacegan: Interpreting the disentangled face representation learned by gans,” TPAMI
2020
Later among the works it cites.
K.-H. Lai, D. Zha, G. Wang, J. Xu, Y. Zhao, D. Kumar, Y. Chen, P. Zumkhawaka, M. Wan, D. Martinez, et al
Original
2020
Later among the works it cites.
D. Zha, K.-H. Lai, M. Wan, and X. Hu, “Meta-aad: Active anomaly detection with deep reinforcement learning,” arXiv preprint arXiv:2009.07415
Original

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

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2018

Cited alongside, same era.

S. Bai et al

Original

2018

Cited alongside, same era.

N. Liu, Q. Tan, Y. Li, H. Yang, J. Zhou, and X. Hu, “Is a single vector enough? exploring node polysemy for network embedding,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Cited alongside, same era.

X. Huang, Q. Song, Y. Li, and X. Hu, “Graph recurrent networks with attributed random walks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining

2019

Cited alongside, same era.

M. Du, S. Pentyala, Y. Li, and X. Hu, “Towards generalizable forgery detection with locality-aware autoencoder,” arXiv preprint arXiv:1909.05999

Original

2019

Cited alongside, same era.

Y. Li, X. Huang, J. Li, M. Du, and N. Zou, “Specae: Spectral autoencoder for anomaly detection in attributed networks,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management

2019

Cited alongside, same era.

Y. Li, N. Liu, J. Li, M. Du, and X. Hu, “Deep structured cross-modal anomaly detection,” in 2019 International Joint Conference on Neural Networks (IJCNN)

2019

Cited alongside, same era.

F. Yang, Z. Zhang, H. Wang, Y. Li, and X. Hu, “Xdeep: An interpretation tool for deep neural networks,” arXiv preprint arXiv:1911.01005

Original

2019

Cited alongside, same era.

2020