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06/07/2023
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
We systematically study a wide variety of image-based generative models ...
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04/26/2023
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation
We propose TR0N, a highly general framework to turn pre-trained uncondit...
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11/30/2022
Denoising Deep Generative Models
Likelihood-based deep generative models have recently been shown to exhi...
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11/23/2022
Relating Regularization and Generalization through the Intrinsic Dimension of Activations
Given a pair of models with similar training set performance, it is natu...
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07/06/2022
The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling
Deep learning has had tremendous success at learning low-dimensional rep...
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06/22/2022
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling
Natural data observed in ℝ^n is often constrained to an m-dimensional ma...
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04/28/2022
On the Normalizing Constant of the Continuous Categorical Distribution
Probability distributions supported on the simplex enjoy a wide range of...
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04/14/2022
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Likelihood-based, or explicit, deep generative models use neural network...
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02/09/2022
Bayesian Nonparametrics for Offline Skill Discovery
Skills or low-level policies in reinforcement learning are temporally ex...
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09/22/2021
Entropic Issues in Likelihood-Based OOD Detection
Deep generative models trained by maximum likelihood remain very popular...
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06/02/2021
Rectangular Flows for Manifold Learning
Normalizing flows are invertible neural networks with tractable change-o...
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11/24/2020
C-Learning: Horizon-Aware Cumulative Accessibility Estimation
Multi-goal reaching is an important problem in reinforcement learning ne...
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11/10/2020
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning
Modern deep learning is primarily an experimental science, in which empi...
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02/20/2020
The continuous categorical: a novel simplex-valued exponential family
Simplex-valued data appear throughout statistics and machine learning, f...
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12/19/2019
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax
The Gumbel-Softmax is a continuous distribution over the simplex that is...
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07/16/2019
The continuous Bernoulli: fixing a pervasive error in variational autoencoders
Variational autoencoders (VAE) have quickly become a central tool in mac...
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03/06/2019
Deep Random Splines for Point Process Intensity Estimation
Gaussian processes are the leading class of distributions on random func...
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01/12/2017