Benefits of Overparameterization in Single-Layer Latent Variable Generative Models.

Rares-Darius Buhai, Andrej Risteski, Yoni Halpern, David Sontag

Type: Article

Publication Date: 2019-06-28

Citations: 4

Locations

arXiv (Cornell University) - View

Similar Works

Action	Title	Year	Authors
+	Empirical Study of the Benefits of Overparameterization in Learning Latent Variable Models	2019	Rares-Darius Buhai Yoni Halpern Yoon Kim Andrej Risteski David Sontag
+ PDF Chat	Learning Multi-layer Latent Variable Model via Variational Optimization of Short Run MCMC for Approximate Inference	2020	Erik Nijkamp Bo Pang Tian Han Linqi Zhou Song‐Chun Zhu Ying Wu
+ PDF Chat	Generative Models for Deep Learning with Very Scarce Data	2019	Juan Maroñas Roberto Paredes Daniel Ramos
+	Layer-wise learning of deep generative models	2012	Ludovic Arnold Yann Ollivier
+ PDF Chat	Non-exhaustive Learning Using Gaussian Mixture Generative Adversarial Networks	2021	Jun Zhuang Mohammad Al Hasan
+	Deep Generative Models with Stick-Breaking Priors	2016	Eric Nalisnick Padhraic Smyth
+	Data-cloning SMC2: A global optimizer for maximum likelihood estimation of latent variable models	2019	Jin‐Chuan Duan András Fülöp Yu‐Wei Hsieh
+	Improving Training of Likelihood-based Generative Models with Gaussian Homotopy	2023	Ba-Hien Tran Giulio Franzese Pietro Michiardi Maurizio Filippone
+	Efficient Estimation with Latent Variables	2019	Michael Bedard
+	Effective Estimation of Deep Generative Language Models	2019	Tom Pelsmaeker Wilker Aziz
+ PDF Chat	Gradient Free Parameter Estimation for Hidden Markov Models with Intractable Likelihoods	2013	Elena Ehrlich Ajay Jasra Nikolas Kantas
+	Slow mixing for Latent Dirichlet Allocation	2017	Johan Jonasson
+	Training Latent Variable Models with Auto-encoding Variational Bayes: A Tutorial	2022	Yang Zhi-Han
+	Improving latent variable descriptiveness with AutoGen	2018	Alex Mansbridge Roberto Fierimonte Ilya Feige David Barber
+ PDF Chat	Private Gradient Estimation is Useful for Generative Modeling	2024	Bochao Liu Pengju Wang Weijia Guo Li Yong Liansheng Zhuang Weiping Wang Shiming Ge
+	Encoding Binary Concepts in the Latent Space of Generative Models for Enhancing Data Representation	2023	Zizhao Hu Mohammad Rostami
+	Rate-Distortion Optimization Guided Autoencoder for Generative Approach with quantitatively measurable latent space.	2019	Keizo Kato Jing Zhou Akira Nakagawa
+	Stacking for Non-mixing Bayesian Computations: The Curse and Blessing of Multimodal Posteriors	2020	Yuling Yao Aki Vehtari Andrew Gelman
+ PDF Chat	Towards Model-Agnostic Posterior Approximation for Fast and Accurate Variational Autoencoders	2024	Yaniv Yacoby Weiwei Pan Finale Doshi‐Velez
+	Importance Weighted Autoencoders	2015	Yuri Burda Roger Grosse Ruslan Salakhutdinov

Works That Cite This (2)

Action	Title	Year	Authors
+	Structures of Spurious Local Minima in $k$-means	2020	Wei Qian Yuqian Zhang Yudong Chen
+	Benefits of Jointly Training Autoencoders: An Improved Neural Tangent Kernel Analysis.	2019	Thanh V. Nguyen Raymond Chi-Wing Wong Chinmay Hegde

Works Cited by This (0)

Action	Title	Year	Authors