随笔分类 - AutoEncoder
摘要：[TOC] > [Ruiz N., Li Y., Jampani V., Pritch Y., Rubinstein M. and Aberman K. DreamBooth: Fine tuning text-to-image diffusion models for subject-driven
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摘要：[TOC] > [Liao R., Li Y., Song Y., Wang S., Nash C., Hamilton W. L., Duvenaud D., Urtasun R. and Zemel R. NIPS, 2019.](http://arxiv.org/abs/1910.00760)
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摘要：[TOC] > [Liu J., Kumar A., Ba J., Kiros J. and Swersky K. Graph normalizing flows. NIPS, 2019.](http://arxiv.org/abs/1905.13177) ## 概 基于 [flows](https
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摘要：[TOC] > [Dinh L, Sohl-Dickstein J. and Bengio S. Density estimation using real nvp. ICLR, 2017.](http://arxiv.org/abs/1605.08803) ## 概 一种可逆的 flow, 感觉很
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摘要：Li Z., Sun A. and Li C. DiffuRec: A diffusion model for sequential recommendation. arXiv preprint arXiv:2304.00686, 2023. 概 扩散模型用于序列推荐, 性能提升很大. DiffuR
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摘要：Song J., Meng C. and Ermon S. Denoising diffusion implicit models. In International Conference on Learning Representations (ICLR), 2021. 概 DDIM 从另一种观点
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摘要：Choi J., Lee J., Shin C., Kim S., Kim H. and Yoon S. Perception prioritized training of diffusion models. In IEEE Computer Vision and Pattern Recognit
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摘要：Gong S., Li M., Feng J., Wu Z. and Kong L. DiffuSeq: Sequence to sequence text generation with diffusion models. In International Conference on Learni
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摘要：Li X. L., Thickstun J., Gulrajani I., Liang P. and Hashimoto T. B. Diffusion-lm improves controllable text generation. arXiv preprint arXiv:2205.14217
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摘要：Austin J., Johnson D. D., Ho J., Tarlow D. and van den Berg R. Structured denoising diffusion models in discrete state-spaces. In Advances in Neural I
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摘要：本文梳理一下 VAE -> Flow -> Diffusion 的过程 [9]. 需要声明的是, 个人是没有进行过这方面的实践的, 相关的理论只是也比较匮乏, 这里只是一个对这些从事贝叶斯网络研究满怀敬意的人的纸上谈兵了. 扩散模型没有被时间洪流所掩埋, 真是让人感动的事情. 符号说明 $\bm{x
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摘要：Alemi A. A., Fischer I., Dillon J. V. and Murphy K. Deep variational information bottleneck. In International Conference on Learning Representations (
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摘要：Liang D., Krishnan R. G., Hoffman M. D. and Jebara T. Variational autoencoders for collaborative filtering. In International Conference on World Wide
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摘要：Song Y., Sohl-Dickstein J., Kingma D. P., Kumar A., Ermon S. and Poole B. Score-based generative modeling through stochastic differential equations. I
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摘要：Sedhain S., Menon A. K., Sanner S. and Xie L. AutoRec: autoencoders meet collaborative filtering. In International Conference on World Wide Web (WWW),
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摘要：Ho J., Jain A. and Abbeel P. Denoising diffusion probabilistic models. In Advances in Neural Information Processing Systems (NIPS), 2020. [Page E. App
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摘要：Song Y. and Ermon S. Generative modeling by estimating gradients of the data distribution. In Advances in Neural Information Processing Systems (NIPS)
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摘要：Khemakhem I., Kingma D. P., Monti R. P. and Hyv"{a}rinen A. Variational autoencoders and nonlinear ICA: a unifying framework. In International Confere
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摘要：Yu Y., Chen J., Gao T. and Yu M. DAG-GNN: DAG structure learning with graph neural networks. In International Conference on Machine Learning (ICML), 2
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摘要：Jang E., Gu S. and Poole B. Categorical reparameterization with gumbel-softmax. In International Conference On Learning Representations (ICLR), 2017.
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