# The Thermodynamic Variational Objective

@inproceedings{Masrani2019TheTV, title={The Thermodynamic Variational Objective}, author={Vaden Masrani and Tuan Anh Le and Frank D. Wood}, booktitle={Neural Information Processing Systems}, year={2019} }

We introduce the thermodynamic variational objective (TVO) for learning in both continuous and discrete deep generative models. The TVO arises from a key connection between variational inference and thermodynamic integration that results in a tighter lower bound to the log marginal likelihood than the standard variational variational evidence lower bound (ELBO) while remaining as broadly applicable. We provide a computationally efficient gradient estimator for the TVO that applies to continuous…

## 33 Citations

### All in the Exponential Family: Bregman Duality in Thermodynamic Variational Inference

- Computer ScienceICML
- 2020

An exponential family interpretation of the geometric mixture curve underlying the TVO and various path sampling methods is proposed, which allows the gap in TVO likelihood bounds as a sum of KL divergences and derives a doubly reparameterized gradient estimator which improves model learning and allows the TVo to benefit from more refined bounds.

### Nested Variational Inference

- Computer ScienceNeurIPS
- 2021

NVI is developed, a family of methods that learn proposals for nested importance samplers by minimizing an forward or reverse KL divergence at each level of nesting, and it is observed that optimizing nested objectives leads to improved sample quality in terms of log average weight and effective sample size.

### Gaussian Process Bandit Optimization of theThermodynamic Variational Objective

- Computer ScienceNeurIPS
- 2020

This paper introduces a bespoke Gaussian process bandit optimization method that automates their one-time selection, but also dynamically adapts their positions over the course of optimization, leading to improved model learning and inference.

### Variational Inference for Sequential Data with Future Likelihood Estimates

- Computer ScienceICML
- 2020

A novel vari ational inference algorithm for sequential data is presented, which performs well even when the density from the model is not differentiable, for instance, due to the use of discrete random variables.

### GFlowNets and variational inference

- Computer Science
- 2022

This paper builds bridges between two families of probabilistic algorithms: (hi-erarchical) variational inference (VI), which is typically used to model distributions over continuous spaces, and…

### NVAE: A Deep Hierarchical Variational Autoencoder

- Computer ScienceNeurIPS
- 2020

NVAE is the first successful VAE applied to natural images as large as 256$\times$256 pixels and achieves state-of-the-art results among non-autoregressive likelihood-based models on the MNIST, CIFAR-10, CelebA 64, and CelebA HQ datasets and it provides a strong baseline on FFHQ.

### Surrogate Likelihoods for Variational Annealed Importance Sampling

- Computer ScienceICML
- 2022

This work argues theoretically that the resulting algorithm allows an intuitive trade-off between inference and computational cost, and shows that it performs well in practice and is well-suited for black-box inference in probabilistic programming frameworks.

### Controlling the Interaction Between Generation and Inference in Semi-Supervised Variational Autoencoders Using Importance Weighting

- Computer ScienceArXiv
- 2020

Using importance weighting and an analysis of the objective of semi-supervised VAEs, it is shown that they use the posterior of the learned generative model to guide the inference model in learning the partially observed latent variable.

### Semi-deterministic and Contrastive Variational Graph Autoencoder for Recommendation

- Computer ScienceCIKM
- 2021

This paper proposes a novel Semi-deterministic and Contrastive Variational Graph autoencoder (SCVG) for item recommendation, and empirically shows that the contrastive regularization makes learned user/item latent representation more personalized and helps to smooth the training process.

### D EEP A TTENTIVE V ARIATIONAL I NFERENCE

- Computer Science

This is the first work that proposes attention mechanisms to build more expressive variational distributions in deep probabilistic models by explicitly modeling both nearby and distant interactions in the latent space and achieves state-of-the-art log-likelihoods while using fewer latent layers and requiring less training time than existing models.

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