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Estimation of Non-Normalized Statistical Models by Score Matching [JMLR 2005]
- Aapo Hyvarinen.
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Extracting and Composing Robust Features with Denoising Autoencoders [ICML 2008]
- Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol.
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A Connection Between Score Matching and Denoising Autoencoders [Neural Computation 2011]
- Pascal Vincent.
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Sliced Score Matching: A Scalable Approach to Density and Score Estimation [UAI 2019][PyTorch]
- Yang Song, Sahaj Garg, Jiaxin Shi, Stefano Ermon.
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Generative Modeling by Estimating Gradients of the Data Distribution [NIPS 2019][PyTorch]
- Yang Song, Stefano Ermon.
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Improved Techniques for Training Score-Based Generative Models [NIPS 2020][PyTorch]
- Yang Song, Stefano Ermon.
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Efficient Learning of Generative Models via Finite-Difference Score Matching [NIPS 2020][PyTorch]
- Tianyu Pang, Kun Xu, Chongxuan Li, Yang Song, Stefano Ermon, Jun Zhu.
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Adversarial score matching and improved sampling for image generation [ICLR 2021][PyTorch]
- Alexia Jolicoeur-Martineau, Rémi Piché-Taillefer, Ioannis Mitliagkas, Remi Tachet des Combes.
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Score-Based Generative Modeling through Stochastic Differential Equations [ICLR 2021][TensorFlow][PyTorch]
- Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, Ben Poole.
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An Introduction to MCMC for Machine Learning [ML 2003]
- Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan.
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Introduction to Markov Chain Monte Carlo [Handbook of Markov Chain Monte Carlo 2010]
- Charles J. Geyer.
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MCMC Using Hamiltonian Dynamics [Handbook of Markov Chain Monte Carlo 2010]
- Radford M. Neal.
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Bayesian Learning via Stochastic Gradient Langevin Dynamics [ICML 2011]
- Max Welling, Yee Whye Teh. (Test-of-time Award of ICML 2021)
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Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring [ICML 2011]
- Sungjin Ahn, Anoop Korattikara, Max Welling. (Best Paper Award)
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Deep Unsupervised Learning using Nonequilibrium Thermodynamics [ICML 2015]
- Jascha Sohl-Dickstein, Eric A. Weiss, Niru Maheswaranathan, Surya Ganguli.
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Denoising Diffusion Probabilistic Models [NIPS 2020][TensorFlow]
- Jonathan Ho, Ajay Jain, Pieter Abbeel.
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Diffusion Models Beat GANs on Image Synthesis [NIPS 2021]
- Prafulla Dhariwal, Alex Nichol.
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Auto-Encoding Variational Bayes [ICLR 2014]
- Diederik P Kingma, Max Welling.
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Generative Adversarial Nets [NIPS 2014]
- Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio.
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[Diffusion Models as a kind of VAE]
- Angus Turner
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- Lilian Weng
https://www.inference.vc/denoising-as-unsupervised-learning/
https://openai.com/blog/generative-models/
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ICML 14 -- Danilo J. Rezende et al.
- (DLGM) Stochastic Backpropagation and Approximate Inference in Deep Generative Models.
- [Paper]
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NIPS 14 -- Diederik P. Kingma et al.
- (SSL-VAE) Semi-Supervised Learning with Deep Generative Models.
- [Paper]
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NIPS 15 -- Kihyuk Sohn et al.
- (CVAE) Learning Structured Output Representation using Deep Conditional Generative Models.
- [Paper & Supplement]
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ICLR 17 oral -- Martin Arjovsky et al.
- (WGAN-1) Towards Principled Methods for Training Generative Adversarial Networks.
- [Paper]
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ICML 17 -- Martin Arjovsky et al.
- (WGAN-2) Wasserstein GAN.
- [Paper]
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ICLR 16 -- Jost Tobias Springenberg.
- (CatGAN) Unsupervised and Semi-supervised Learning with Categorical Generative Adversarial Networks.
- [Paper]
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NIPS 16 -- Sebastian Nowozin et al.
- f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization.
- [Paper]
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NIPS 16 -- Xi Chen et al.
- InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets.
- [Paper]
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ICLR 18 -- Zhiting Hu et al.
- On Unifying Deep Generative Models.
- [Paper]
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SIGIR 17 Best Paper-- Jun Wang et al.
- (IRGAN)IRGAN: A Minimax Game for Unifying Generative and Discriminative Information Retrieval Models.
- [Paper]
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WWW 18 -- Dawen Liang et al.
- Variational Autoencoders for Collaborative Filtering.
- [Paper]
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IJCAI 17 -- Zhuxi Jiang et al.
- (VaDE) Variational Deep Embedding: An Unsupervised and Generative Approach to Clustering.
- [Paper]
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AAAI 18 -- Hongwei Wang et al.
- (GraphGAN) GraphGAN: Graph Representation Learning with Generative Adversarial Nets.
- [Paper]
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AAAI 19 -- Sudipto Mukherjee et al.
- (ClusterGAN) ClusterGAN: Latent Space Clustering in Generative Adversarial Networks.
- [Paper]
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NIPS 18 -- Yucen Luo et al.
- Semi-crowdsourced Clustering with Deep Generative Models.
- [Paper]
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JMLR 13 -- Matthew D. Hoffman et al.
- (SVI) Stochastic Variational Inference.
- [Paper]
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ICLR 18 -- Ilya Tolstikhin et al.
- Wasserstein Auto-Encoders.
- [Paper]
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JASA 17 -- David M.Blei et al.
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NIPS 16 Tutorial -- David Blei et al.
- Variational Inference: Foundations and Modern Methods.
- [Slides & Video]
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NIPS 16 Tutorial -- Ian Goodfellow et al.
- Generative Adversarial Networks
- [Slides & Video]
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Arxiv 16 -- Carl Doersch.
- Tutorial on Variational Autoencoders.
- [Paper]
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IJCAI 18 Tutorial -- Stefano Ermon et al.
- Tutorial on Deep Generative Models
- [Slides]
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UAI 17 Tutorial -- Shakir Mohamed and Danilo Rezende.
- Deep Generative Models
- [Slides & Video]
-
Hung-yi Lee
- Machine Learning and having it deep and structured (2018, Spring, National Taiwan University)
- [Notes & Video]
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Stefano Ermon, Aditya Grover
- CS 236: Deep Generative Models (2018, Fall, Stanford University)
- [Notes]
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PRML
- Pattern Recognition and Machine Learning. Christopher Bishop.
- [Book & Support]