Gabriel Loaiza-Ganem

According to our database1, Gabriel Loaiza-Ganem authored at least 23 papers between 2017 and 2023.

Collaborative distances:
  • Dijkstra number2 of five.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
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Links

On csauthors.net:

Bibliography

2023
Data-Efficient Multimodal Fusion on a Single GPU.
CoRR, 2023

Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models.
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.
Proceedings of the International Conference on Machine Learning, 2023

Verifying the Union of Manifolds Hypothesis for Image Data.
Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models.
Trans. Mach. Learn. Res., 2022

CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds.
CoRR, 2022

Relating Regularization and Generalization through the Intrinsic Dimension of Activations.
CoRR, 2022

The Union of Manifolds Hypothesis and its Implications for Deep Generative Modelling.
CoRR, 2022

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling.
CoRR, 2022

On the Normalizing Constant of the Continuous Categorical Distribution.
CoRR, 2022

Bayesian Nonparametrics for Offline Skill Discovery.
Proceedings of the International Conference on Machine Learning, 2022

Denoising Deep Generative Models.
Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

2021
Rectangular Flows for Manifold Learning.
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.
Proceedings of the 9th International Conference on Learning Representations, 2021

Entropic Issues in Likelihood-Based OOD Detection.
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.
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.
Proceedings of the 37th International Conference on Machine Learning, 2020

Uses and Abuses of the Cross-Entropy Loss: Case Studies in Modern Deep Learning.
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.
PhD thesis, 2019

Deep Random Splines for Point Process Intensity Estimation of Neural Population Data.
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.
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.
Proceedings of the Deep Generative Models for Highly Structured Data, 2019

2017
Maximum Entropy Flow Networks.
Proceedings of the 5th International Conference on Learning Representations, 2017


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