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| 论文代码尝试.md | 219 B | 2024-Jun-20 16:32 |
| 论文下载情况.jpg | 141.9 KiB | 2024-Jun-03 15:06 |
| 3{manifold learning}[55](v)2012(J)CS_Izenman_Introduction to manifold learning.pdf | 240.8 KiB | 2024-Jun-03 21:08 |
| 4{latent space of VAEs}[76](v)2018_arXiv_Pineau_InfoCatVAE Representation learning with categorical variational autoencoders.pdf | 676.5 KiB | 2024-Jun-17 12:30 |
| 8{mixture models}[88]2021(J)NNLS_Ye_Deep mixture generative autoencoders.pdf | 1.1 MiB | 2024-Jun-03 14:31 |
| 8{mixture models}[86](v)2022(J)IVC_Stolberg-Larsen_Atlas generative models and geodesic interpolation.pdf | 1.3 MiB | 2024-Jun-03 14:31 |
| 1{Manifold hypothesis}[16](v)2013_arXiv_Bengio_Representation learning A review and new perspectives.pdf | 1.5 MiB | 2024-Jun-03 19:54 |
| 9{manifold learning, VAEs}[4]2022_arXiv_Alberti_Continuous generative neural networks.pdf | 1.5 MiB | 2024-Jun-03 15:20 |
| 5.1{Whitney embedding theorem}[70]2022(J)IP_Massa_Approximation of discontinuous inverse operators with neural networks.pdf | 1.7 MiB | 2024-Jun-03 14:31 |
| 6{cluster algorithms, linear embeddings}[77](v)2013(P)CVPR_Pitelis_Learning a manifold as an atlas.pdf | 1.8 MiB | 2024-Jun-20 14:11 |
| 7{generative models}[81]2019_arXiv_Schonsheck_Chart auto-encoders for manifold structured data.pdf | 2.1 MiB | 2024-Jun-03 14:31 |
| 5{latent distributions of VAEs}[78](v)2020(P)ICAI_Rey_Diffusion variational autoencoders.pdf | 2.1 MiB | 2024-Jun-18 14:43 |
| 5{latent distributions of VAEs}[31](v)2018(P)CUAI_Davidson_Hyperspherical variational auto-encoders.pdf | 2.1 MiB | 2024-Jun-17 12:30 |
| 6{cluster algorithms, learn for each chart}[83](v)2022_arXiv_Sidheekh_VQ-Flows Vector quantized local normalizing flows.pdf | 2.2 MiB | 2024-Jun-20 11:24 |
| 7{generative models}[61]2018_arXiv_Korman_Autoencoding topology.pdf | 2.5 MiB | 2024-Jun-03 14:31 |
| 2{variational autoencoders}[58](v)2013_arXiv_Kingma_Auto-encoding variational Bayes.pdf | 3.8 MiB | 2024-Jun-03 20:14 |
| 8{mixture models}[51]2017_arXiv_Hoang_Multi-generator generative adversarial nets.pdf | 4.5 MiB | 2024-Jun-03 14:31 |
| 5{latent distributions of VAEs}[71](v)2019(J)NIPS_Mathieu_Continuous hierarchical representations with Poincare variational auto-encoders.pdf | 4.8 MiB | 2024-Jun-17 14:37 |
| 9{manifold learning, VAEs}[39]2022(J)JIS_Gonzalez_Solving inverse problems by joint posterior maximization with autoencoding prior.pdf | 4.8 MiB | 2024-Jun-03 15:20 |
| 9{manifold learning, VAEs}[33]2021(J)JMIV_Duff_Regularising inverse problems with generative machine learning models.pdf | 5.7 MiB | 2024-Jun-03 15:20 |
| 6{cluster algorithms}[29](v)2021_arXiv_Cohn_Topologically-informed atlas learning.pdf | 6.2 MiB | 2024-Jun-20 15:14 |
| 8{mixture models}[65]2018_arXiv_Locatello_Competitive training of mixture of independent deep generative models.pdf | 6.9 MiB | 2024-Jun-03 14:31 |
| 8{Mixture models}[13](v)2017(P)_IJCNN_Banijamali_Generative mixture of networks.pdf | 7.5 MiB | 2024-Jun-24 21:47 |
| 9{manifold learning, VAEs}[23]2020(P)ICML_Chen_Learning flat latent manifolds with vaes.pdf | 7.8 MiB | 2024-Jun-03 15:20 |
| 6{cluster algorithms}[38]2022(P)NMI_Floryan_Data-driven discovery of intrinsic dynamics(TC).pdf | 9.6 MiB | 2024-Jun-03 14:31 |
| 7{generative models}[62]2021_arXiv_Korman_Atlas based representation and metric learning on manifolds.pdf | 9.6 MiB | 2024-Jun-03 14:31 |
| 3{manifold learning}[68]2011(B)CRC press_Ma_Manifold learning theory and applications.pdf.pdf | 10.1 MiB | 2024-Jun-03 21:34 |
| _2023_arXiv_Alberti_Manifold learning by mixture models of vaes for inverse problems.pdf | 17.5 MiB | 2024-Jun-20 16:31 |
