Amortised Inference in Structured Generative Models with Explaining Away

No abstract is available for this paper.

• Publication year

  2022

• Venue

  arXiv.org

• Publication date

  Unknown publication date

• Fields of study

  Mathematics, Computer Science

• Identifiers
  DOI 10.48550/arXiv.2209.05212
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• Geometry of abstract learned knowledge in the hippocampus

  2021cited by this paper
• Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning

  2021cited by this paper
• Generative Adversarial Networks

  2021cited by this paper
• tvGP-VAE: Tensor-variate Gaussian Process Prior Variational Autoencoder

  2020cited by this paper
• Gradient Estimation with Stochastic Softmax Tricks

  2020influential reference
• Sparse Gaussian Process Variational Autoencoders

  2020influential reference
• PyTorch: An Imperative Style, High-Performance Deep Learning Library

  2019cited by this paper
• Efficient non-conjugate Gaussian process factor models for spike count data using polynomial approximations

  2019cited by this paper
• Variational Message Passing with Structured Inference Networks

  2018influential reference
• Gaussian Process Prior Variational Autoencoders

  2018cited by this paper
• Tighter Variational Bounds are Not Necessarily Better

  2018cited by this paper
• Training VAEs Under Structured Residuals

  2018cited by this paper
• Graphical Generative Adversarial Networks

  2018influential reference
• The Gaussian Process Autoregressive Regression Model (GPAR)

  2018cited by this paper
• Temporal alignment and latent Gaussian process factor inference in population spike trains

  2018influential reference
• Approximate Inference with Amortised MCMC

  2017cited by this paper
• Gaussian process based nonlinear latent structure discovery in multivariate spike train data

  2017cited by this paper
• The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables

  2016cited by this paper
• Deep Unsupervised Clustering with Gaussian Mixture Variational Autoencoders

  2016influential reference
• Categorical Reparameterization with Gumbel-Softmax

  2016cited by this paper
• Composing graphical models with neural networks for structured representations and fast inference

  2016influential reference
• Scalable transformed additive signal decomposition by non-conjugate Gaussian process inference

  2016influential reference
• Conditional Image Generation with PixelCNN Decoders

  2016cited by this paper
• DRAW: A Recurrent Neural Network For Image Generation

  2015cited by this paper
• The Variational Gaussian Process

  2015cited by this paper
• Dimensionality reduction for large-scale neural recordings

  2014cited by this paper
• Stochastic Backpropagation and Approximate Inference in Deep Generative Models

  2014influential reference
• Structured Stochastic Variational Inference

  2014cited by this paper
• Adam: A Method for Stochastic Optimization

  2014cited by this paper
• Auto-Encoding Variational Bayes

  2013influential reference
• Variational Learning of Inducing Variables in Sparse Gaussian Processes

  2009influential reference
• Graphical Models, Exponential Families, and Variational Inference

  2008cited by this paper
• Place cells, grid cells, and the brain's spatial representation system.

  2008cited by this paper
• Gaussian-process factor analysis for low-dimensional single-trial analysis of neural population activity

  2008influential reference
• Faculty Opinions recommendation of Conjunctive representation of position, direction, and velocity in entorhinal cortex.

  2006cited by this paper
• Variational inference for Dirichlet process mixtures

  2006cited by this paper
• Variational Message Passing

  2005cited by this paper
• Canonical Correlation Analysis: An Overview with Application to Learning Methods

  2004cited by this paper
• Gaussian Process Latent Variable Models for Visualisation of High Dimensional Data

  2003cited by this paper
• Inferring Parameters and Structure of Latent Variable Models by Variational Bayes

  1999cited by this paper
• Exploiting Tractable Substructures in Intractable Networks

  1995cited by this paper
• Probabilistic reasoning in intelligent systems: Networks of plausible inference

  1991cited by this paper
• The contributions of position, direction, and velocity to single unit activity in the hippocampus of freely-moving rats

  1983cited by this paper
• The Hippocampus as a Cognitive Map

  1980cited by this paper
• Maximum likelihood from incomplete data via the EM - algorithm plus discussions on the paper

  1977cited by this paper

• Unbiased Learning of Deep Generative Models with Structured Discrete Representations

  2023cites this paper
• Multi-modal Gaussian Process Variational Autoencoders for Neural and Behavioral Data

  2023cites this paper