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https://doi.org/10.2139/ssrn.4102842

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/978-3-030-58574-7_10openalex-extracted
High-Fidelity Synthesis with Disentangled Representation
inferredcites →https://doi.org/10.1016/j.eswa.2010.06.073openalex-extracted
Conceptual modeling of causal map: Object oriented causal map
inferredcites →https://doi.org/10.1016/j.eswa.2021.115895openalex-extracted
A framework for feature selection through boosting
inferredcites →https://doi.org/10.1073/pnas.1309933111openalex-extracted
Equitability, mutual information, and the maximal information coefficient
inferredcites →https://doi.org/10.1109/cvpr46437.2021.00947openalex-extracted
CausalVAE: Disentangled Representation Learning via Neural Structural Causal Models
inferredcites →https://doi.org/10.1109/iccv.2017.299openalex-extracted
CVAE-GAN: Fine-Grained Image Generation through Asymmetric Training
inferredcites →https://doi.org/10.1109/wacv.2018.00136openalex-extracted
Channel-Recurrent Autoencoding for Image Modeling
inferredcites →https://doi.org/10.1609/aaai.v36i7.20781openalex-extracted
On Causally Disentangled Representations
inferredcites →https://doi.org/10.48550/arxiv.1312.6114openalex-extracted
Auto-Encoding Variational Bayes
inferredcites →https://doi.org/10.48550/arxiv.1512.09300openalex-extracted
Autoencoding beyond pixels using a learned similarity metric
inferredcites →https://doi.org/10.48550/arxiv.1606.03657openalex-extracted
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets
inferredcites →https://doi.org/10.48550/arxiv.1703.10155openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1706.08500openalex-extracted
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
inferredcites →https://doi.org/10.48550/arxiv.1709.02023openalex-extracted
CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training
inferredcites →https://doi.org/10.48550/arxiv.1711.00848openalex-extracted
Variational Inference of Disentangled Latent Concepts from Unlabeled Observations
inferredcites →https://doi.org/10.48550/arxiv.1802.04942openalex-extracted
Isolating Sources of Disentanglement in Variational Autoencoders
inferredcites →https://doi.org/10.48550/arxiv.1802.05983openalex-extracted
Disentangling by Factorising
inferredcites →https://doi.org/10.48550/arxiv.1804.03599openalex-extracted
Understanding disentangling in $β$-VAE
inferredcites →https://doi.org/10.48550/arxiv.1904.10098openalex-extracted
DAG-GNN: DAG Structure Learning with Graph Neural Networks
inferredcites →https://doi.org/10.48550/arxiv.1905.01258openalex-extracted
Disentangling Factors of Variation Using Few Labels
inferredcites →https://doi.org/10.48550/arxiv.1910.08527openalex-extracted
inferredcites →https://doi.org/10.48550/arxiv.1910.09772openalex-extracted
Weakly Supervised Disentanglement with Guarantees
inferredcites →https://doi.org/10.48550/arxiv.1911.07420openalex-extracted
A Graph Autoencoder Approach to Causal Structure Learning
inferredcites →https://doi.org/10.48550/arxiv.2002.02886openalex-extracted
Weakly-Supervised Disentanglement Without Compromises
inferredcites →https://doi.org/10.48550/arxiv.2002.09161openalex-extracted
Bidirectional Generative Modeling Using Adversarial Gradient Estimation
inferredcites →https://doi.org/10.48550/arxiv.2102.11107openalex-extracted
Towards Causal Representation Learning
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