Gabriel Loaiza-Ganem

• 32. A Geometric Explanation of the Likelihood OOD Detection Paradox

H Kamkari, BL Ross, JC Cresswell, AL Caterini, RG Krishnan, G Loaiza-Ganem

ICML 2024

[paper][code]

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

G Loaiza-Ganem, BL Ross, R Hosseinzadeh, AL Caterini, JC Cresswell

arXiv preprint

[paper][code]

• 30. Data-Efficient Multimodal Fusion on a Single GPU

N Vouitsis, Z Liu, SK Gorti, V Villecroze, JC Cresswell, G Yu, G Loaiza-Ganem, M Volkovs

CVPR 2024 (highlight)

[paper][code]

• 29. A Geometric Explanation of the Likelihood Out-of-Distribution Detection Paradox

H Kamkari, BL Ross, JC Cresswell, AL Caterini, RG Krishnan, G Loaiza-Ganem

ICLR 2024 GenAI4DM workshop

• 28. Neural Implicit Manifold Learning for Topology-Aware Density Estimation

BL Ross, G Loaiza-Ganem, AL Caterini, JC Cresswell

TMLR 2023 (expert certification)

[paper][code]

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

G Stein, JC Cresswell, R Hosseinzadeh, Y Sui, BL Ross, V Villecroze, Z Liu, AL Caterini*, JET Taylor*, G Loaiza-Ganem*

NeurIPS 2023

[paper][code]

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

Z Liu, N Vouitsis, SK Gorti, J Ba, G Loaiza-Ganem

ICML 2023

[paper][code][demo]

• 25. Verifying the Union of Manifolds Hypothesis for Image Data

BCA Brown, AL Caterini, BL Ross, JC Cresswell, G Loaiza-Ganem

ICLR 2023

[paper][code]

• 24. Denoising Deep Generative Models

G Loaiza-Ganem, BL Ross, L Wu, JP Cunningham, JC Cresswell, AL Caterini

NeurIPS 2022 ICBINB workshop (spotlight)

[paper][code]

• 23. The Union of Manifolds Hypothesis

BCA Brown, AL Caterini, BL Ross, JC Cresswell, G Loaiza-Ganem

NeurIPS 2022 SGNR workshop

[paper][code]

• 22. Relating Regularization and Generalization through the Intrinsic Dimension of Activations

BCA Brown, J Juravsky, AL Caterini, G Loaiza-Ganem

NeurIPS 2022 OPT workshop

[paper]

• 21. Relating Regularization and Generalization through the Intrinsic Dimension of Activations

BCA Brown, J Juravsky, AL Caterini, G Loaiza-Ganem

NeurIPS 2022 HITY workshop

[paper]

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

JC Cresswell, BL Ross, G Loaiza-Ganem, H Reyes-Gonzalez, M Letizia, AL Caterini

NeurIPS 2022 ML4PS workshop

[paper]

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

JC Cresswell, BL Ross, G Loaiza-Ganem, H Reyes-Gonzalez, M Letizia, AL Caterini

ML4Jets 2022

[paper]

• 18. On a novel probability distribution for zero-laden compositional data

E Gordon-Rodriguez, G Loaiza-Ganem, JP Cunningham

CoDaWork 2022 (oral)

• 17. Diagnosing and Fixing Manifolds Overfitting in Deep Generative Models

G Loaiza-Ganem, BL Ross, JC Cresswell, AL Caterini

TMLR 2022 (expert certification)

[paper][code]

• 16. Bayesian Nonparametrics for Offline Skill Discovery

V Villecroze, HJ Braviner, P Naderian, CJ Maddison, G Loaiza-Ganem

ICML 2022

[paper][code]

• 15. Neural Implicit Manifold Learning for Topology-Aware Generative Modelling

BL Ross, G Loaiza-Ganem, AL Caterini, JC Cresswell

ICML 2022 TAG workshop

[paper]

• 14. On the Normalizing Constant of the Continuous Categorical Distribution

E Gordon-Rodriguez*, G Loaiza-Ganem*, A Potapczynski, JP Cunningham

arXiv preprint

[paper][code]

• 13. Rectangular Flows for Manifold Learning

AL Caterini*, G Loaiza-Ganem*, G Pleiss, JP Cunningham

NeurIPS 2021

[paper][code]

• 12. Entropic Issues in Likelihood-Based OOD Detection

AL Caterini, G Loaiza-Ganem

NeurIPS 2021 ICBINB worshop (spotlight)

[paper]

• 11. Rectangular Flows for Manifold Learning

AL Caterini*, G Loaiza-Ganem*, G Pleiss, JP Cunningham

ICML 2021 INNF workshop (spotlight)

[paper][code]

• 10. C-Learning: Horizon-Aware Cumulative Accessibility Estimation

P Naderian, G Loaiza-Ganem,  HJ Braviner, AL Caterini, JC Cresswell, T Li, A Garg

ICLR 2021

[paper][code]

• 9. Invertible Gaussian Reparameterization Trick: Revisiting the Gumbel-Softmax

A Potapczynski, G Loaiza-Ganem, JP Cunningham

NeurIPS 2020

[paper][code]

• 8. C-Learning: Horizon-Aware Cumulative Accessibility Estimation

P Naderian, G Loaiza-Ganem, HJ Braviner, AL Caterini, JC Cresswell, T Li, A Garg

NeurIPS 2020 DRL workshop

[paper][code]

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

E Gordon-Rodriguez, G Loaiza-Ganem, G Pleiss, JP Cunningham

NeurIPS 2020 ICBINB workshop (oral)

[paper][code]

• 6. The Continuous Categorical: A Novel Simplex-Valued Exponential Family

E Gordon-Rodriguez, G Loaiza-Ganem, JP Cunningham

ICML 2020

[paper][code]

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

G Loaiza-Ganem, JP Cunningham

NeurIPS 2019

[paper][code]

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

G Loaiza-Ganem, SM Perkins, KE Schroeder, MM Churchland, JP Cunningham

NeurIPS 2019

[paper][code]

• 3. Deep Random Splines for Point Process Intensity Estimation

G Loaiza-Ganem, JP Cunningham

ICLR 2019 DeepGenStruct workshop

[paper][code]

• 2. Latent structure in populations of truly continuous spike trains: inference with deep random splines

G Loaiza-Ganem, JP Cunningham

Cosyne 2019

[paper]

• 1. Maximum Entropy Flow Networks

G Loaiza-Ganem*, Y Gao*, JP Cunningham

ICLR 2017

[paper][code]