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Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations

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Jul 01 2019 Tejas Kulkarni

Our work shows that adding geometric inductive biases in neural nets enables spatio-temporally consistent (hundreds of steps) object keypoints. This enables agents that play Atari games on a single machine with less than 100k steps + deeply explore hard envs without rewards. https://twitter.com/deepmindai/status/1145677732115898368
5 replies, 276 likes


Jun 15 2019 Reza Zadeh

Best paper award at #ICML2019 main idea: unsupervised learning of disentangled representations is fundamentally impossible without inductive biases. Verified theoretically & experimentally. https://arxiv.org/pdf/1811.12359.pdf
0 replies, 206 likes


Feb 20 2019 Olivier Bachem

#OpenScience: We are happy to announce the release of >10'000 pretrained disentanglement_lib models (https://github.com/google-research/disentanglement_lib#pretrained-disentanglement_lib-modules) from the study “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representation” (https://arxiv.org/abs/1811.12359). @GoogleAI https://t.co/G3TDSO5UMb
2 replies, 159 likes


Feb 13 2019 Olivier Bachem

Interested in doing research on disentanglement? We just open-sourced disentanglement_lib (https://github.com/google-research/disentanglement_lib), the library we built for our study “Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representation” (https://arxiv.org/abs/1811.12359) @GoogleAI https://t.co/2wG9ZRxHNW
3 replies, 153 likes


Apr 11 2019 Olivier Bachem

Excited that "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" (https://arxiv.org/abs/1811.12359) was accepted for oral presentation + poster at the #ICLR2019 Workshop on Reproducibility in Machine Learning! https://t.co/JOF0RwHMCe
2 replies, 104 likes


Apr 23 2019 Olivier Bachem

“Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations” (https://arxiv.org/abs/1811.12359) was accepted to #ICML2019. Happy that reviewers do value extensive experiments! @FrancescoLocat8 @MarioLucic_ @sylvain_gelly @gxr @GoogleAI @ETH_en @MPI_IS
0 replies, 100 likes


Jun 11 2019 DataScienceNigeria

Congratulations to the Best Papers at the ongoing #ICML2019 (1)Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations https://arxiv.org/pdf/1811.12359.pdf (2)Rates of Convergence for Sparse Variational Gaussian Process Regression https://arxiv.org/pdf/1903.03571.pdf https://t.co/0DlXj6gcWb
2 replies, 74 likes


Oct 07 2019 Vadim Kantorov

@zacharylipton There is "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" (https://arxiv.org/abs/1811.12359) by Google people, mostly about VAEs
0 replies, 39 likes


Oct 07 2019 augustus odena

@zacharylipton Not sure a survey exists that does a good job covering all of those but https://arxiv.org/abs/1811.12359 is quite nice (disentangling in context of VAEs) and I have *sort of* a GAN survey https://distill.pub/2019/gan-open-problems/ that talks about disentangling-adjacent things?
0 replies, 35 likes


Jun 13 2019 Paul Liang

2 great papers at #ICML2019 study this theoretical and empirically: Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations (https://arxiv.org/abs/1811.12359), and Disentangling Disentanglement in Variational Autoencoders (https://arxiv.org/abs/1812.02833)
1 replies, 17 likes


Jun 11 2019 Gautam Kamath

There were two best paper awards. One was "Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations" by @FrancescoLocat8, Stefan Bauer, @MarioLucic_, @gxr, @sylvain_gelly, @bschoelkopf , @OlivierBachem (https://arxiv.org/abs/1811.12359) 2/n
1 replies, 13 likes


Jun 11 2019 reza mahmoudi

ICML 2019 Best Paper Award by @icmlconf 1. http://arxiv.org/abs/1811.12359 2. http://arxiv.org/abs/1903.03571 @Montreal_AI #ICML2019 #ai #Machinelearning #DeepLearning https://t.co/Zlx6aMg5TN
0 replies, 10 likes


Jun 11 2019 Hyrum Anderson

Best paper award at #ICML2019 claims that unsupervised learning of disentangled representations for arbitrary data is impossible without inductive bias. Paper: https://arxiv.org/abs/1811.12359 https://t.co/ipcH0DYZqg
3 replies, 9 likes


Jun 13 2019 Reza Zadeh

Best paper award at #ICML2019 main idea: learning disentangled representations in an unsupervised manner is theoretically impossible & empirically very challenging. There's no free lunch with unsupervised learning on Computer Vision. https://arxiv.org/pdf/1811.12359.pdf https://t.co/Pyklengchh
0 replies, 3 likes


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