| The continuous Bernoulli: fixing a pervasive error in variational autoencoders G Loaiza-Ganem, JP Cunningham Advances in Neural Information Processing Systems 32, 2019 | 103 | 2019 |
| Uses and abuses of the cross-entropy loss: Case studies in modern deep learning E Gordon-Rodriguez, G Loaiza-Ganem, G Pleiss, JP Cunningham PMLR, 2020 | 101* | 2020 |
| 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, ... arXiv preprint arXiv:2306.04675, 2023 | 70 | 2023 |
| Verifying the Union of Manifolds Hypothesis for Image Data BCA Brown, AL Caterini, BL Ross, JC Cresswell, G Loaiza-Ganem International Conference on Learning Representations, 2023 | 57* | 2023 |
| Rectangular flows for manifold learning AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham Advances in Neural Information Processing Systems 34, 30228-30241, 2021 | 45 | 2021 |
| CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds JC Cresswell, BL Ross, G Loaiza-Ganem, H Reyes-Gonzalez, M Letizia, ... arXiv preprint arXiv:2211.15380, 2022 | 40* | 2022 |
| Invertible gaussian reparameterization: Revisiting the gumbel-softmax A Potapczynski, G Loaiza-Ganem, JP Cunningham Advances in Neural Information Processing Systems 33, 12311-12321, 2020 | 33 | 2020 |
| The continuous categorical: a novel simplex-valued exponential family E Gordon-Rodriguez, G Loaiza-Ganem, J Cunningham International Conference on Machine Learning, 3637-3647, 2020 | 32 | 2020 |
| Maximum entropy flow networks G Loaiza-Ganem, Y Gao, JP Cunningham International Conference on Learning Representations, 2017 | 30 | 2017 |
| Diagnosing and fixing manifold overfitting in deep generative models G Loaiza-Ganem, BL Ross, JC Cresswell, AL Caterini Transactions on Machine Learning Research, 2022 | 26 | 2022 |
| Entropic issues in likelihood-based ood detection AL Caterini, G Loaiza-Ganem I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 21-26, 2022 | 22 | 2022 |
| A geometric view of data complexity: Efficient local intrinsic dimension estimation with diffusion models H Kamkari, BL Ross, R Hosseinzadeh, JC Cresswell, G Loaiza-Ganem arXiv preprint arXiv:2406.03537, 2024 | 12 | 2024 |
| 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 arXiv:2404.02954, 2024 | 11 | 2024 |
| Deep random splines for point process intensity estimation of neural population data G Loaiza-Ganem, S Perkins, K Schroeder, M Churchland, ... Advances in Neural Information Processing Systems 32, 2019 | 11 | 2019 |
| Neural implicit manifold learning for topology-aware density estimation BL Ross, G Loaiza-Ganem, AL Caterini, JC Cresswell Transactions on Machine Learning Research, 2022 | 10* | 2022 |
| A geometric explanation of the likelihood OOD detection paradox H Kamkari, BL Ross, JC Cresswell, AL Caterini, RG Krishnan, ... arXiv preprint arXiv:2403.18910, 2024 | 9 | 2024 |
| Denoising Deep Generative Models G Loaiza-Ganem, BL Ross, L Wu, JP Cunningham, JC Cresswell, ... PMLR, 2023 | 8 | 2023 |
| Bayesian nonparametrics for offline skill discovery V Villecroze, H Braviner, P Naderian, C Maddison, G Loaiza-Ganem International Conference on Machine Learning, 22284-22299, 2022 | 8 | 2022 |
| Calochallenge 2022: A community challenge for fast calorimeter simulation C Krause, MF Giannelli, G Kasieczka, B Nachman, D Salamani, D Shih, ... arXiv preprint arXiv:2410.21611, 2024 | 7 | 2024 |
| A geometric framework for understanding memorization in generative models BL Ross, H Kamkari, T Wu, R Hosseinzadeh, Z Liu, G Stein, JC Cresswell, ... arXiv preprint arXiv:2411.00113, 2024 | 5 | 2024 |