# Learning Deep Generative Models

@inproceedings{Salakhutdinov2009LearningDG, title={Learning Deep Generative Models}, author={Ruslan Salakhutdinov}, year={2009} }

Building intelligent systems that are capable of extracting high-level representations from high-dimensional sensory data lies at the core of solving many AI related tasks, including object recognition, speech perception, and language understanding. Theoretical and biological arguments strongly suggest that building such systems requires models with deep architectures that involve many layers of nonlinear processing.
The aim of the thesis is to demonstrate that deep generative models that…

## 347 Citations

### Deep learning systems as complex networks

- Computer ScienceJournal of Complex Networks
- 2019

This article proposes to study deep belief networks using techniques commonly employed in the study of complex networks, in order to gain some insights into the structural and functional properties of the computational graph resulting from the learning process.

### Inference in Deep Networks in High Dimensions

- Computer Science2018 IEEE International Symposium on Information Theory (ISIT)
- 2018

The main contribution shows that the mean-squared error (MSE) of ML-VAMP can be exactly predicted in a certain large system limit and matches the Bayes optimal value recently postulated by Reeves when certain fixed point equations have unique solutions.

### Predictive learning as a network mechanism for extracting low-dimensional latent space representations

- Computer Science, PsychologyNature communications
- 2021

This work investigates the hypothesis that a means for generating representations with easily accessed low-dimensional latent structure is through learning to predict observations about the world, and investigates whether and when network mechanisms for sensory prediction coincide with those for extracting the underlying latent variables.

### Learning Deep and Wide: A Spectral Method for Learning Deep Networks

- Computer ScienceIEEE Transactions on Neural Networks and Learning Systems
- 2014

This work proposes the multispectral neural networks (MSNN) to learn features from multicolumn deep neural networks and embed the penultimate hierarchical discriminative manifolds into a compact representation.

### Signatures and mechanisms of low-dimensional neural predictive manifolds

- Computer Science, BiologybioRxiv
- 2018

This work investigates the hypothesis that the hippocampus performs its role in sequential planning by organizing semantically related episodes in a relational network from learning a predictive representation of the world, and shows that network dynamics exhibit low dimensional but non-linearly transformed representations of sensory input statistics.

### Generative learning for deep networks

- Computer ScienceArXiv
- 2017

It is shown that forward computation in DNNs with logistic sigmoid activations corresponds to a simplified approximate Bayesian inference in a directed probabilistic multi-layer model, and proposed that in order for the recognition and generation networks to be more consistent with the joint model of the data, weights of the Recognition and generator network should be related by transposition.

### An Overview of Deep Generative Models

- Computer Science
- 2015

Three important deep generative models including DBNs, deep autoencoder, and deep Boltzmann machine are reviewed and some successful applications of deep generatives models in image processing, speech recognition and information retrieval are introduced and analysed.

### Novel deep generative simultaneous recurrent model for efficient representation learning

- Computer ScienceNeural Networks
- 2018

### Inference With Deep Generative Priors in High Dimensions

- Computer ScienceIEEE Journal on Selected Areas in Information Theory
- 2020

This paper shows that the performance of ML-VAMP can be exactly predicted in a certain high-dimensional random limit, and provides a computationally efficient method for multi-layer inference with an exact performance characterization and testable conditions for optimality in the large-system limit.

### Generative mixture of networks

- Computer Science2017 International Joint Conference on Neural Networks (IJCNN)
- 2017

A generative model based on training deep architectures that consists of K networks that are trained together to learn the underlying distribution of a given data set, called Mixture of Networks, has high capability in characterizing complicated data distributions as well as clustering data.

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