Anthony L. Caterini

According to our database1, Anthony L. Caterini authored at least 26 papers between 2015 and 2024.

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

Timeline

Legend:

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PhD thesis 
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Links

On csauthors.net:

Bibliography

2024
Neural Implicit Manifold Learning for Topology-Aware Density Estimation.
Trans. Mach. Learn. Res., 2024

TabPFGen - Tabular Data Generation with TabPFN.
CoRR, 2024

Retrieval & Fine-Tuning for In-Context Tabular Models.
CoRR, 2024

Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections.
CoRR, 2024

In-Context Data Distillation with TabPFN.
CoRR, 2024

A Geometric Explanation of the Likelihood OOD Detection Paradox.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

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

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

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

2021
Variational inference with continuously-indexed normalizing flows.
Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 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
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows.
Proceedings of the 37th International Conference on Machine Learning, 2020

2019
Detecting anthropogenic cloud perturbations with deep learning.
CoRR, 2019

Localised Generative Flows.
CoRR, 2019

2018
Deep Neural Networks in a Mathematical Framework
Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-75303-4, 2018

Hamiltonian Variational Auto-Encoder.
Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2016
A Novel Representation of Neural Networks.
CoRR, 2016

A Geometric Framework for Convolutional Neural Networks.
CoRR, 2016

2015
Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark.
Proceedings of the 21st IEEE International Conference on Parallel and Distributed Systems, 2015


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