
A Variational Perspective on DiffusionBased Generative Models and Score Matching
Discretetime diffusionbased generative models and score matching metho...
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Convex Potential Flows: Universal Probability Distributions with Optimal Transport and Convex Optimization
Flowbased models are powerful tools for designing probabilistic models ...
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RealCause: Realistic Causal Inference Benchmarking
There are many different causal effect estimators in causal inference. H...
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ARDAE: Towards Unbiased Neural Entropy Gradient Estimation
Entropy is ubiquitous in machine learning, but it is in general intracta...
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Augmented Normalizing Flows: Bridging the Gap Between Generative Flows and Latent Variable Models
In this work, we propose a new family of generative flows on an augmente...
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Investigating Biases in Textual Entailment Datasets
The ability to understand logical relationships between sentences is an ...
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vGraph: A Generative Model for Joint Community Detection and Node Representation Learning
This paper focuses on two fundamental tasks of graph analysis: community...
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Stochastic Neural Network with Kronecker Flow
Recent advances in variational inference enable the modelling of highly ...
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Note on the bias and variance of variational inference
In this note, we study the relationship between the variational gap and ...
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Hierarchical Importance Weighted Autoencoders
Importance weighted variational inference (Burda et al., 2015) uses mult...
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Improving Explorability in Variational Inference with Annealed Variational Objectives
Despite the advances in the representational capacity of approximate dis...
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Neural Autoregressive Flows
Normalizing flows and autoregressive models have been successfully combi...
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Generating Contradictory, Neutral, and Entailing Sentences
Learning distributed sentence representations remains an interesting pro...
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Neural Language Modeling by Jointly Learning Syntax and Lexicon
We propose a neural language model capable of unsupervised syntactic str...
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Bayesian Hypernetworks
We propose Bayesian hypernetworks: a framework for approximate Bayesian ...
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Learnable Explicit Density for Continuous Latent Space and Variational Inference
In this paper, we study two aspects of the variational autoencoder (VAE)...
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ChinWei Huang
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