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Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models.

K. Märtens, K. Campbell, и C. Yau. ICML, том 97 из Proceedings of Machine Learning Research, стр. 4372-4381. PMLR, (2019)

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Hans Märtens

Erwin Märtens

Manola Märtens

Susanne Märtens

Otto Märtens

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Disentangling shared and private latent factors in multimodal Variational Autoencoders.K. Märtens, и C. Yau. MLCB, том 240 из Proceedings of Machine Learning Research, стр. 60-75. PMLR, (2023)Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models.K. Märtens, K. Campbell, и C. Yau. ICML, том 97 из Proceedings of Machine Learning Research, стр. 4372-4381. PMLR, (2019)Neural Decomposition: Functional ANOVA with Variational Autoencoders.K. Märtens, и C. Yau. CoRR, (2020)Disentangling shared and private latent factors in multimodal Variational Autoencoders.K. Märtens, и C. Yau. CoRR, (2024)Augmented Ensemble MCMC sampling in Factorial Hidden Markov Models.K. Märtens, M. Titsias, и C. Yau. AISTATS, том 89 из Proceedings of Machine Learning Research, стр. 2359-2367. PMLR, (2019)Rarity: discovering rare cell populations from single-cell imaging data.K. Märtens, M. Bortolomeazzi, L. Montorsi, J. Spencer, F. Ciccarelli, и C. Yau. Bioinform., (декабря 2023)Covariate Gaussian Process Latent Variable Models.K. Märtens, K. Campbell, и C. Yau. CoRR, (2018)BasisVAE: Translation-invariant feature-level clustering with Variational Autoencoders.K. Märtens, и C. Yau. AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 2928-2937. PMLR, (2020)Deep Stochastic Processes via Functional Markov Transition Operators.J. Xu, E. Dupont, K. Märtens, T. Rainforth, и Y. Teh. CoRR, (2023)

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