Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations | Paper Citation Record

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This paper cites Learning factorial codes by pre- dictability minimization,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Learning factorial codes by pre- dictability minimization,

Reference 1

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Observation d7e48c2e-da26-45f1-8d6a-e7e05cd92ac5 · outbound

This paper cites A Survey of Inductive Biases for Factorial Representation-Learning.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations A Survey of Inductive Biases for Factorial Representation-Learning

Reference 2

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This paper cites Emergence of Invariance and Disentanglement in Deep Representations.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Emergence of Invariance and Disentanglement in Deep Representations

Reference 3

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This paper cites β-vae: Learning basic visual concepts with a con- strained variational framework,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations β-vae: Learning basic visual concepts with a con- strained variational framework,

Reference 4

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This paper cites Un- derstanding disentangling in β-vae,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Un- derstanding disentangling in β-vae,

Reference 5

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Observation 51f6f3f6-9a49-4aa8-a303-e5aebce409ad · outbound

This paper cites Isolating Sources of Disentanglement in Variational Autoencoders.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Isolating Sources of Disentanglement in Variational Autoencoders

Reference 6

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This paper cites Disentangling by Factorising.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Disentangling by Factorising

Reference 7

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This paper cites Auto-encoding variational bayes,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Auto-encoding variational bayes,

Reference 8

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This paper cites Infogan: Inter- pretable representation learning by information maximizing generative adversarial nets,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Infogan: Inter- pretable representation learning by information maximizing generative adversarial nets,

Reference 9

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This paper cites Sugiyama, T.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Sugiyama, T

Reference 10

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This paper cites Variational Inference of Disentangled Latent Concepts from Unlabeled Observations.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Variational Inference of Disentangled Latent Concepts from Unlabeled Observations

Reference 11

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This paper cites Generative Models of Visually Grounded Imagination.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Generative Models of Visually Grounded Imagination

Reference 12

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This paper cites Deep convolutional inverse graph- ics network,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Deep convolutional inverse graph- ics network,

Reference 14

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This paper cites Semi-supervised learning with deep generative models,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Semi-supervised learning with deep generative models,

Reference 15

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This paper cites Learning to disentangle factors of variation with manifold interaction,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Learning to disentangle factors of variation with manifold interaction,

Reference 16

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This paper cites Automatic diﬀerentiation in machine learning: a survey.,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Automatic diﬀerentiation in machine learning: a survey.,

Reference 17

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This paper cites Transforming auto-encoders,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Transforming auto-encoders,

Reference 18

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Observation 6e5f8f54-b64b-4b97-a789-62c4a440f916 · outbound

This paper cites Unpaired image-to-image translation using cycle- consistent adversarial networks,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unpaired image-to-image translation using cycle- consistent adversarial networks,

Reference 19

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This paper cites Unsupervised learning of spatiotem- porally coherent metrics,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unsupervised learning of spatiotem- porally coherent metrics,

Reference 20

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This paper cites Unsuper- vised learning of disentangled and interpretable representations from sequential data,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unsuper- vised learning of disentangled and interpretable representations from sequential data,

Reference 21

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This paper cites Unsupervised learning of disentangled representations from video,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unsupervised learning of disentangled representations from video,

Reference 22

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This paper cites VAE with a VampPrior.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations VAE with a VampPrior

Reference 23

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This paper cites The LORACs prior for VAEs: Letting the Trees Speak for the Data.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations The LORACs prior for VAEs: Letting the Trees Speak for the Data

Reference 24

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This paper cites Chal- lenging common assumptions in the unsupervised learning of disentangled representations,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Chal- lenging common assumptions in the unsupervised learning of disentangled representations,

Reference 25

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This paper cites Vari- ational autoencoders pursue pca directions (by accident),.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Vari- ational autoencoders pursue pca directions (by accident),

Reference 26

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This paper cites Two problems with variational expectation maximisation for time- series models,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Two problems with variational expectation maximisation for time- series models,

Reference 27

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This paper cites Importance Weighted Autoencoders.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Importance Weighted Autoencoders

Reference 28

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This paper cites Inference Suboptimality in Variational Autoencoders.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Inference Suboptimality in Variational Autoencoders

Reference 29

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This paper cites The generalized gaus- sian mixture model using ica,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations The generalized gaus- sian mixture model using ica,

Reference 30

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This paper cites Self- adaptive blind source separation based on activa- tion functions adaptation,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Self- adaptive blind source separation based on activa- tion functions adaptation,

Reference 31

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This paper cites Eﬃcient coding of natural sounds,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Eﬃcient coding of natural sounds,

Reference 32

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This paper cites Lp-nested symmetric distributions,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Lp-nested symmetric distributions,

Reference 33

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unresolved cited work

Reference 34

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This paper cites dsprites: Disentanglement testing sprites dataset.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations dsprites: Disentanglement testing sprites dataset

Reference 35

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This paper cites Character- ization of the p-generalized normal distribution,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Character- ization of the p-generalized normal distribution,

Reference 36

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This paper cites Emergence of phase-and shift-invariant features by decomposi- tion of natural images into independent feature subspaces,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Emergence of phase-and shift-invariant features by decomposi- tion of natural images into independent feature subspaces,

Reference 37

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This paper cites Complex cell pool- ing and the statistics of natural images,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Complex cell pool- ing and the statistics of natural images,

Reference 38

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This paper cites Hier- archical modeling of local image features through Lp-nested symmetric distributions,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Hier- archical modeling of local image features through Lp-nested symmetric distributions,

Reference 39

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This paper cites Modeling and inference with υ-spherical distri- butions,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Modeling and inference with υ-spherical distri- butions,

Reference 40

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This paper cites Towards a Definition of Disentangled Representations.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Towards a Definition of Disentangled Representations

Reference 41

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This paper cites Stick- ing the landing: Simple, lower-variance gradi- ent estimators for variational inference,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Stick- ing the landing: Simple, lower-variance gradi- ent estimators for variational inference,

Reference 42

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unresolved cited work

Reference 43

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This paper cites Deep visual analogy-making,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Deep visual analogy-making,

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:02:21.380254Z

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This paper cites Adam: A method for stochastic optimization,.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Adam: A method for stochastic optimization,

Reference 45

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unresolved cited work

Reference 46

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This paper cites Obtain coordinates on the Lp-nested sphere within the positive orthant by si↦→ s 1 pi i = ˜ui (the exponentiation is taken component-wise).

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Obtain coordinates on the Lp-nested sphere within the positive orthant by si↦→ s 1 pi i = ˜ui (the exponentiation is taken component-wise)

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:02:21.330247Z

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This paper cites The components of vi,1:li constitute the radii for the layer direct below them.

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations The components of vi,1:li constitute the radii for the layer direct below them

Reference 48

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This paper cites Normalize x to get a uniform sample from the sphere u = x f (x).

Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Normalize x to get a uniform sample from the sphere u = x f (x)

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-14T05:02:21.298550Z

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unresolved cited work

Reference 50

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Independent Subspace Analysis for Unsupervised Learning of Disentangled Representations Unresolved cited work

Reference 51

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