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09/28/2022
Score Modeling for Simulation-based Inference
Neural Posterior Estimation methods for simulation-based inference can b...
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09/24/2021
Unbiased Gradient Estimation with Balanced Assignments for Mixtures of Experts
Training large-scale mixture of experts models efficiently on modern har...
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06/15/2021
Coupled Gradient Estimators for Discrete Latent Variables
Training models with discrete latent variables is challenging due to the...
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01/26/2021
Generalized Doubly Reparameterized Gradient Estimators
Efficient low-variance gradient estimation enabled by the reparameteriza...
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06/18/2020
DisARM: An Antithetic Gradient Estimator for Binary Latent Variables
Training models with discrete latent variables is challenging due to the...
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06/08/2020
The Lipschitz Constant of Self-Attention
Lipschitz constants of neural networks have been explored in various con...
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01/22/2020
Q-Learning in enormous action spaces via amortized approximate maximization
Applying Q-learning to high-dimensional or continuous action spaces can ...
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10/23/2019
Sparse Orthogonal Variational Inference for Gaussian Processes
We introduce a new interpretation of sparse variational approximations f...
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06/25/2019
Monte Carlo Gradient Estimation in Machine Learning
This paper is a broad and accessible survey of the methods we have at ou...
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01/17/2019
Attentive Neural Processes
Neural Processes (NPs) (Garnelo et al 2018a;b) approach regression by le...
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10/26/2018
Resampled Priors for Variational Autoencoders
We propose Learned Accept/Reject Sampling (LARS), a method for construct...
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05/22/2018
Implicit Reparameterization Gradients
By providing a simple and efficient way of computing low-variance gradie...
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02/16/2018
Disentangling by Factorising
We define and address the problem of unsupervised learning of disentangl...
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05/25/2017
Filtering Variational Objectives
When used as a surrogate objective for maximum likelihood estimation in ...
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03/21/2017
REBAR: Low-variance, unbiased gradient estimates for discrete latent variable models
Learning in models with discrete latent variables is challenging due to ...
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11/02/2016
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
The reparameterization trick enables optimizing large scale stochastic c...
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02/22/2016
Variational inference for Monte Carlo objectives
Recent progress in deep latent variable models has largely been driven b...
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11/16/2015
MuProp: Unbiased Backpropagation for Stochastic Neural Networks
Deep neural networks are powerful parametric models that can be trained ...
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01/31/2014
Neural Variational Inference and Learning in Belief Networks
Highly expressive directed latent variable models, such as sigmoid belie...
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09/27/2011