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Inductive biases are crucial in disentangled representation learning for narrowing down an underspecified solution set.
A stochastic estimator of the trace of the influence matrix for laplacian smoothing splines
Hutchinson, M. F · 1989
Earlier work this paper cites.
Independent component analysis, a new concept?
Comon, P · 1994
Earlier work this paper cites.
Density estimation for statistics and data analysis
Silverman, B. W · 1998
Earlier work this paper cites.
The multiinformation function as a tool for measuring stochastic dependence
Studenỳ, M. and Vejnarová, J · 1998
Earlier work this paper cites.
Nonlinear independent component analysis: existence and uniqueness results
Hyvärinen, A. and Pajunen, P · 1999
Earlier work this paper cites.
Independent component analysis: algorithms and applications
Hyvärinen, A. and Oja, E · 2000
Earlier work this paper cites.
Scikit-learn: machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Édouard Duchesnay · 2011
Earlier work this paper cites.
Deep learning of representations: looking forward
Bengio, Y · 2013
Earlier work this paper cites.
Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
Earlier work this paper cites.
Generative adversarial networks
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
Earlier work this paper cites.
Auto-encoding variational Bayes
Kingma, D. P. and Welling, M · 2014
Earlier work this paper cites.
Understanding disentangling in β \beta -VAE
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2017
Earlier work this paper cites.
β \beta -VAE: learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017
Earlier work this paper cites.
Categorical reparameterization with Gumbel-Softmax
Jang, E., Gu, S., and Poole, B · 2017
Earlier work this paper cites.
The concrete distribution: a continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2017
Earlier work this paper cites.
Neural discrete representation learning
Oord, A. v. d., Vinyals, O., and Kavukcuoglu, K · 2017
Cited alongside, same era.
JAX: composable transformations of Python+NumPy programs, 2018
Bradbury, J., Frostig, R., Hawkins, P., Johnson, M. J., Leary, C., Maclaurin, D., Necula, G., Paszke, A., VanderPlas, J., Wanderman-Milne, S., and Zhang, Q · 2018
Cited alongside, same era.
3D shapes dataset, 2018
Burgess, C. and Kim, H · 2018
Cited alongside, same era.
Isolating sources of disentanglement in variational autoencoders
Chen, R. T., Li, X., Grosse, R. B., and Duvenaud, D. K · 2018
Cited alongside, same era.
A framework for the quantitative evaluation of disentangled representations
Eastwood, C. and Williams, C. K · 2018
Cited alongside, same era.
Disentangling by factorising
Kim, H. and Mnih, A · 2018
Cited alongside, same era.
Diffusion models beat GANs on image synthesis
Dhariwal, P. and Nichol, A · 2021
Later among the works it cites.
Independent mechanism analysis, a new concept?
Gresele, L., Von Kügelgen, J., Stimper, V., Schölkopf, B., and Besserve, M · 2021
Later among the works it cites.
When is unsupervised disentanglement possible?
Horan, D., Richardson, E., and Weiss, Y · 2021
Later among the works it cites.
Equinox: neural networks in JAX via callable PyTrees and filtered transformations
Kidger, P. and Garcia, C · 2021
Later among the works it cites.
Orthogonal Jacobian regularization for unsupervised disentanglement in image generation
Wei, Y., Shi, Y., Liu, X., Ji, Z., Gao, Y., Wu, Z., and Zuo, W · 2021
Later among the works it cites.
Identifiable deep generative models via sparse decoding
Moran, G. E., Sridhar, D., Wang, Y., and Blei, D · 2022
Later among the works it cites.
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Variational inference of disentangled latent concepts from unlabeled observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
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On the transfer of inductive bias from simulation to the real world: a new disentanglement dataset
Gondal, M. W., Wuthrich, M., Miladinovic, D., Locatello, F., Breidt, M., Volchkov, V., Akpo, J., Bachem, O., Schölkopf, B., and Bauer, S · 2019
Cited alongside, same era.
Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Raetsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2019
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High resolution disentanglement datasets, 2019
Nie, W · 2019
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Array programming with NumPy
Harris, C. R., Millman, K. J., Van Der Walt, S. J., Gommers, R., Virtanen, P., Cournapeau, D., Wieser, E., Taylor, J., Berg, S., Smith, N. J., et al · 2020
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Implicit rank-minimizing autoencoder
Jing, L., Zbontar, J., et al · 2020
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Interpretable machine learning: fundamental principles and 10 grand challenges
Rudin, C., Chen, C., Chen, Z., Huang, H., Semenova, L., and Zhong, C · 2022
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Nonlinear ICA using volume-preserving transformations
Yang, X., Yang, Y., Sun, J., Zhang, X., Zhang, S., Li, Z., and Yan, J · 2022
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Disentangled sequence to sequence learning for compositional generalization
Zheng, H. and Lapata, M · 2022
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On the identifiability of nonlinear ICA: sparsity and beyond
Zheng, Y., Ng, I., and Zhang, K · 2022
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Disentanglement via latent quantization
Hsu, K., Dorrell, W., Whittington, J. C., Wu, J., and Finn, C · 2023
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Structure by architecture: structured representations without regularization
Leeb, F., Lanzillotta, G., Annadani, Y., Besserve, M., Bauer, S., and Schölkopf, B · 2023
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Measuring interpretability of neural policies of robots with disentangled representation
Wang, T.-H., Xiao, W., Seyde, T., Hasani, R., and Rus, D · 2023
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Disentanglement with biological constraints: a theory of functional cell types
Whittington, J. C. R., Dorrell, W., Ganguli, S., and Behrens, T · 2023
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Finite scalar quantization: VQ-VAE made simple
Mentzer, F., Minnen, D., Agustsson, E., and Tschannen, M · 2024
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