Gabriel Loaiza-Ganem

According to our database, Gabriel Loaiza-Ganem authored at least 27 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Neural Implicit Manifold Learning for Topology-Aware Density Estimation.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

Trans. Mach. Learn. Res., 2024

A Geometric View of Data Complexity: Efficient Local Intrinsic Dimension Estimation with Diffusion Models.

Hamidreza Kamkari, Brendan Leigh Ross, Rasa Hosseinzadeh, Jesse C. Cresswell, Gabriel Loaiza-Ganem

CoRR, 2024

Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell

CoRR, 2024

A Geometric Explanation of the Likelihood OOD Detection Paradox.

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Data-Efficient Multimodal Fusion on a Single GPU.

Noël Vouitsis, Zhaoyan Liu, Satya Krishna Gorti, Valentin Villecroze, Jesse C. Cresswell, Guangwei Yu, Gabriel Loaiza-Ganem, Maksims Volkovs

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.

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

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation.

Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem

Proceedings of the International Conference on Machine Learning, 2023

Verifying the Union of Manifolds Hypothesis for Image Data.

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini

Trans. Mach. Learn. Res., 2022

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds.

Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini

CoRR, 2022

Relating Regularization and Generalization through the Intrinsic Dimension of Activations.

Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem

CoRR, 2022

The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling.

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

CoRR, 2022

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

CoRR, 2022

On the Normalizing Constant of the Continuous Categorical Distribution.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham

CoRR, 2022

Bayesian Nonparametrics for Offline Skill Discovery.

Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem

Proceedings of the International Conference on Machine Learning, 2022

Denoising Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini

Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

2021

Rectangular Flows for Manifold Learning.

Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

C-Learning: Horizon-Aware Cumulative Accessibility Estimation.

Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg

Proceedings of the 9th International Conference on Learning Representations, 2021

Entropic Issues in Likelihood-Based OOD Detection.

Anthony L. Caterini, Gabriel Loaiza-Ganem

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax.

Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

The continuous categorical: a novel simplex-valued exponential family.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the 37th International Conference on Machine Learning, 2020

Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning.

Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

Proceedings of the "I Can't Believe It's Not Better!" at NeurIPS Workshops, 2020

2019

Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience.

Gabriel Loaiza-Ganem

PhD thesis, 2019

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data.

Gabriel Loaiza-Ganem, Sean Perkins, Karen Schroeder, Mark M. Churchland, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

The continuous Bernoulli: fixing a pervasive error in variational autoencoders.

Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Deep Random Splines for Point Process Intensity Estimation.

Gabriel Loaiza-Ganem, John P. Cunningham

Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2017

Maximum Entropy Flow Networks.

Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham

Proceedings of the 5th International Conference on Learning Representations, 2017