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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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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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04/14/2022
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models
Likelihood-based, or explicit, deep generative models use neural network...
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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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09/30/2019
Localised Generative Flows
We argue that flow-based density models based on continuous bijections a...
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05/29/2018
Hamiltonian Variational Auto-Encoder
Variational Auto-Encoders (VAEs) have become very popular techniques to ...
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08/15/2016