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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 - [i23]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) - [i22]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) - [i21]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) - [i20]Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang:
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. CoRR abs/2410.21611 (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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last updated on 2024-12-03 20:32 CET by the dblp team
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