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ORCIDGabriel Loaiza-Ganem
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2020 – today
- 2024
[j2]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)- [c17]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. CVPR 2024: 27229-27241 - [c16]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. ICML 2024 - [i22]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 abs/2403.18910 (2024) - [i21]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 abs/2404.02954 (2024) - [i20]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 abs/2406.03537 (2024) - 2023
- [c15]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 - [c14]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: 22092-22112 - [c13]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 - [i19]Zhaoyan Liu, Noël Vouitsis, Satya Krishna Gorti, Jimmy Ba, Gabriel Loaiza-Ganem:
TR0N: Translator Networks for 0-Shot Plug-and-Play Conditional Generation. CoRR abs/2304.13742 (2023) - [i18]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 abs/2306.04675 (2023) - [i17]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 abs/2312.10144 (2023) - 2022
- [j1]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) - [c12]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. ICBINB 2022: 41-50 - [c11]Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem:
Bayesian Nonparametrics for Offline Skill Discovery. ICML 2022: 22284-22299 - [i16]Valentin Villecroze, Harry J. Braviner, Panteha Naderian, Chris J. Maddison, Gabriel Loaiza-Ganem:
Bayesian Nonparametrics for Offline Skill Discovery. CoRR abs/2202.04675 (2022) - [i15]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini:
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR abs/2204.07172 (2022) - [i14]Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, Andres Potapczynski, John P. Cunningham:
On the Normalizing Constant of the Continuous Categorical Distribution. CoRR abs/2204.13290 (2022) - [i13]Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell:
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR abs/2206.11267 (2022) - [i12]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 abs/2207.02862 (2022) - [i11]Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem:
Relating Regularization and Generalization through the Intrinsic Dimension of Activations. CoRR abs/2211.13239 (2022) - [i10]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 abs/2211.15380 (2022) - [i9]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. CoRR abs/2212.01265 (2022) - 2021
- [c10]Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS 2021: 21-26 - [c9]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 - [c8]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. NeurIPS 2021: 30228-30241 - [i8]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. CoRR abs/2106.01413 (2021) - [i7]Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. CoRR abs/2109.10794 (2021) - 2020
- [c7]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: 1-10 - [c6]Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous categorical: a novel simplex-valued exponential family. ICML 2020: 3637-3647 - [c5]Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham:
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. NeurIPS 2020 - [i6]Elliott Gordon-Rodríguez, Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous categorical: a novel simplex-valued exponential family. CoRR abs/2002.08563 (2020) - [i5]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 abs/2011.05231 (2020) - [i4]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 abs/2011.12363 (2020)
2010 – 2019
- 2019
- [b1]Gabriel Loaiza-Ganem:
Advances in Deep Generative Modeling With Applications to Image Generation and Neuroscience. Columbia University, USA, 2019 - [c4]Gabriel Loaiza-Ganem, John P. Cunningham:
Deep Random Splines for Point Process Intensity Estimation. DGS@ICLR 2019 - [c3]Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. NeurIPS 2019: 13266-13276 - [c2]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: 13346-13356 - [i3]Gabriel Loaiza-Ganem, John P. Cunningham:
Deep Random Splines for Point Process Intensity Estimation. CoRR abs/1903.02610 (2019) - [i2]Gabriel Loaiza-Ganem, John P. Cunningham:
The continuous Bernoulli: fixing a pervasive error in variational autoencoders. CoRR abs/1907.06845 (2019) - [i1]Andres Potapczynski, Gabriel Loaiza-Ganem, John P. Cunningham:
Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax. CoRR abs/1912.09588 (2019) - 2017
- [c1]Gabriel Loaiza-Ganem, Yuanjun Gao, John P. Cunningham:
Maximum Entropy Flow Networks. ICLR (Poster) 2017
Coauthor Index
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