Fetching the paper…
Reading the bibliography…
In this study, we present meta-sequential prediction (MSP), an unsupervised framework to learn the symmetry from the time sequence of length at least three.
Hamiltonian systems and transformation in hilbert space
B. O. Koopman · 1931
Earlier work this paper cites.
Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition
K. Fukushima and S. Miyake · 1982
Earlier work this paper cites.
Representation Theory: A First Course , volume 129
W. Fulton and J. Harris · 1991
Earlier work this paper cites.
Fast Fourier Transforms
M. Clausen and U. Baum · 1993
Earlier work this paper cites.
A classification of multiplicity free representations
A. S. Leahy · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Independent component analysis: algorithms and applications
A. Hyvärinen and E. Oja · 2000
Earlier work this paper cites.
Representation Theory of Finite Groups: Algebra and Arithmetic , volume 59
S. H. Weintraub · 2003
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. J. Huang, and L. Bottou · 2004
Earlier work this paper cites.
Group theoretical methods in machine learning
I. R. Kondor · 2008
Earlier work this paper cites.
Learning transport operators for image manifolds
B. Culpepper and B. Olshausen · 2009
Earlier work this paper cites.
Rectified linear units improve restricted boltzmann machines
V. Nair and G. E. Hinton · 2010
Earlier work this paper cites.
An unsupervised algorithm for learning lie group transformations
J. Sohl-Dickstein, C. M. Wang, and B. A. Olshausen · 2010
Earlier work this paper cites.
Deep sparse rectifier neural networks
X. Glorot, A. Bordes, and Y. Bengio · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Earlier work this paper cites.
Rectifier nonlinearities improve neural network acoustic models
A. L. Maas, A. Y. Hannun, and A. Y. Ng · 2013
Earlier work this paper cites.
Learning the irreducible representations of commutative lie groups
T. Cohen and M. Welling · 2014
Earlier work this paper cites.
Dynamic mode decomposition for real-time background/foreground separation in video
J. Grosek and J. N. Kutz · 2014
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. Kingma and J. Ba · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton · 2016
Earlier work this paper cites.
Group equivariant convolutional networks
T. Cohen and M. Welling · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Unsupervised feature extraction by time-contrastive learning and nonlinear ica
A. Hyvarinen and H. Morioka · 2016
Cited alongside, same era.
Semi-supervised classification with graph convolutional networks
T. Kipf and M. Welling · 2016
Cited alongside, same era.
Wavenet: A generative model for raw audio
A. Van den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu · 2016
Cited alongside, same era.
Understanding disentangling in β \beta -vae
C. P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, G. Desjardins, and A. Lerchner · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
Contrastive learning of structured world models
T. Kipf, E. van der Pol, and M. Welling · 2020
Later among the works it cites.
Disentangling by subspace diffusion
D. Pfau, I. Higgins, A. Botev, and S. Racanière · 2020
Later among the works it cites.
Learning disentangled representations and group structure of dynamical environments
R. Quessard, T. Barrett, and W. Clements · 2020
Later among the works it cites.
Mdp homomorphic networks: Group symmetries in reinforcement learning
E. van der Pol, D. Worrall, H. van Hoof, F. Oliehoek, and M. Welling · 2020
Later among the works it cites.
Noether networks: meta-learning useful conserved quantities
F. Alet, D. Doblar, A. Zhou, J. Tenenbaum, K. Kawaguchi, and C. Finn · 2021
Later among the works it cites.
Koopman operator dynamical models: Learning, analysis and control
P. Bevanda, S. Sosnowski, and S. Hirche · 2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
Cited alongside, same era.
Attention is all you need
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, Ł. Kaiser, and I. Polosukhin · 2017
Cited alongside, same era.
3d shapes dataset
C. Burgess and H. Kim · 2018
Cited alongside, same era.
Explorations in homeomorphic variational auto-encoding
L. Falorsi, P. de Haan, T. R. Davidson, N. De Cao, M. Weiler, P. Forré, and T. S. Cohen · 2018
Cited alongside, same era.
Towards a definition of disentangled representations
I. Higgins, D. Amos, D. Pfau, S. Racaniere, L. Matthey, D. Rezende, and A. Lerchner · 2018
Cited alongside, same era.
Disentangling by factorising
H. Kim and A. Mnih · 2018
Cited alongside, same era.
Representation learning with contrastive predictive coding
A. Van den Oord, Y. Li, and O. Vinyals · 2018
Cited alongside, same era.
Later among the works it cites.
Addressing the topological defects of disentanglement via distributed operators
D. Bouchacourt, M. Ibrahim, and S. Deny · 2021
Later among the works it cites.
Automatic symmetry discovery with lie algebra convolutional network
N. Dehmamy, R. Walters, Y. Liu, D. Wang, and R. Yu · 2021
Later among the works it cites.
Vector neurons: A general framework for so(3)-equivariant networks
C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas · 2021
Later among the works it cites.
Simone: View-invariant, temporally-abstracted object representations via unsupervised video decomposition
R. Kabra, D. Zoran, G. Erdogan, L. Matthey, A. Creswell, M. Botvinick, A. Lerchner, and C. Burgess · 2021
Later among the works it cites.
Conditional object-centric learning from video
T. Kipf, G. F. Elsayed, A. Mahendran, A. Stone, S. Sabour, G. Heigold, R. Jonschkowski, A. Dosovitskiy, and K. Greff · 2021
Later among the works it cites.
Towards nonlinear disentanglement in natural data with temporal sparse coding
D. Klindt, L. Schott, Y. Sharma, I. Ustyuzhaninov, W. Brendel, M. Bethge, and D. Paiton · 2021
Later among the works it cites.
Neural descriptor fields: Se (3)-equivariant object representations for manipulation
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann · 2021
Later among the works it cites.
Self-supervised learning with data augmentations provably isolates content from style
J. Von Kügelgen, Y. Sharma, L. Gresele, W. Brendel, B. Schölkopf, M. Besserve, and F. Locatello · 2021
Later among the works it cites.
Translating math formula images to latex sequences using deep neural networks with sequence-level training
Z. Wang and J.-C. Liu · 2021
Later among the works it cites.
Towards building a group-based unsupervised representation disentanglement framework
T. Yang, X. Ren, Y. Wang, W. Zeng, and N. Zheng · 2021
Later among the works it cites.
Cross-view gait recognition with deep universal linear embeddings
S. Zhang, Y. Wang, and A. Li · 2021
Later among the works it cites.
Contrastive learning inverts the data generating process
R. S. Zimmermann, Y. Sharma, S. Schneider, M. Bethge, and W. Brendel · 2021
Later among the works it cites.
Kubric: a scalable dataset generator
K. Greff, F. Belletti, L. Beyer, C. Doersch, Y. Du, D. Duckworth, D. J. Fleet, D. Gnanapragasam, F. Golemo, C. Herrmann, T. Kipf, A. Kundu, D. Lagun, I. Laradji, H.-T. D. Liu, H. Meyer, Y. Miao, D. Nowrouzezahrai, C. Oztireli, E. Pot, N. Radwan, D. Rebain, S. Sabour, M. S. M. Sajjadi, M. Sela, V. Sitzmann, A. Stone, D. Sun, S. Vora, Z. Wang, T. Wu, K. M. Yi, F. Zhong, and A. Tagliasacchi · 2022
Closest in time.
Symmetry-based representations for artificial and biological general intelligence
I. Higgins, S. Racanière, and D. Rezende · 2022
Closest in time.
Invariance-adapted decomposition and lasso-type contrastive learning
M. Koyama, T. Miyato, and K. Fukumizu · 2022
Closest in time.
So(2) -equivariant reinforcement learning
D. Wang, R. Walters, and R. Platt · 2022
Closest in time.