Gabriel Loaiza-Ganem

Publication Activity (10 Years)

Years Active: 2017-2024
Publications (10 Years): 39

Top Topics

Manifold Learning

Diffusion Models

Density Estimation

Intrinsic Dimension

Top Venues

Publications

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections. CoRR (2024)
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
Neural Implicit Manifold Learning for Topology-Aware Density Estimation. Trans. Mach. Learn. Res. 2024 (2024)
Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem
A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models. CoRR (2024)
Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem
A Geometric Explanation of the Likelihood OOD Detection Paradox. CoRR (2024)
Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. CoRR (2023)
Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs
Data-Efficient Multimodal Fusion on a Single GPU. CoRR (2023)
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
Verifying the Union of Manifolds Hypothesis for Image Data. ICLR (2023)
George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. NeurIPS (2023)
Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. ICML (2023)
George Stein, Jesse C. Cresswell, Rasa Hosseinzadeh, Yi Sui, Brendan Leigh Ross, Valentin Villecroze, Zhaoyan Liu, Anthony L. Caterini, J. Eric T. Taylor, Gabriel Loaiza-Ganem
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models. CoRR (2023)
Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
Bayesian Nonparametrics for Offline Skill Discovery. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR (2022)
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR (2022)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham
On the Normalizing Constant of the Continuous Categorical Distribution. CoRR (2022)
Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem
The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling. CoRR (2022)
Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem
Bayesian Nonparametrics for Offline Skill Discovery. ICML (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. CoRR (2022)
Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem
Relating Regularization and Generalization through the Intrinsic Dimension of Activations. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. ICBINB (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. Trans. Mach. Learn. Res. 2022 (2022)
Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds. CoRR (2022)
Anthony L. Caterini, Gabriel Loaiza-Ganem
Entropic Issues in Likelihood-Based OOD Detection. CoRR (2021)
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. ICLR (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. CoRR (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. NeurIPS (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem
Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS (2021)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. CoRR (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
The continuous categorical: a novel simplex-valued exponential family. CoRR (2020)
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. NeurIPS (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning. ICBINB@NeurIPS (2020)
Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham
The continuous categorical: a novel simplex-valued exponential family. ICML (2020)
Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg
C-Learning: Horizon-Aware Cumulative Accessibility Estimation. CoRR (2020)
Gabriel Loaiza-Ganem, John P. Cunningham
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. CoRR (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation. CoRR (2019)
Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation of Neural Population Data. NeurIPS (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS (2019)
Gabriel Loaiza-Ganem, John P. Cunningham
Deep Random Splines for Point Process Intensity Estimation. DGS@ICLR (2019)
Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. CoRR (2019)
Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham
Maximum Entropy Flow Networks. ICLR (Poster) (2017)