Anthony L. Caterini

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

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Neural Implicit Manifold Learning for Topology-Aware Density Estimation.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

Trans. Mach. Learn. Res., 2024

TabPFGen - Tabular Data Generation with TabPFN.

Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, Anthony L. Caterini

CoRR, 2024

Retrieval & Fine-Tuning for In-Context Tabular Models.

Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini

CoRR, 2024

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

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Rasa Hosseinzadeh, Anthony L. Caterini, Jesse C. Cresswell

CoRR, 2024

In-Context Data Distillation with TabPFN.

Junwei Ma, Valentin Thomas, Guangwei Yu, Anthony L. Caterini

CoRR, 2024

A Geometric Explanation of the Likelihood OOD Detection Paradox.

Hamidreza Kamkari, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini, Rahul G. Krishnan, Gabriel Loaiza-Ganem

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.

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

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.

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

Proceedings of the Eleventh International Conference on Learning Representations, 2023

2022

Diagnosing and Fixing Manifold Overfitting in Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini

Trans. Mach. Learn. Res., 2022

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

Jesse C. Cresswell, Brendan Leigh Ross, Gabriel Loaiza-Ganem, Humberto Reyes-González, Marco Letizia, Anthony L. Caterini

CoRR, 2022

Relating Regularization and Generalization through the Intrinsic Dimension of Activations.

Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem

CoRR, 2022

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

Bradley C. A. Brown, Anthony L. Caterini, Brendan Leigh Ross, Jesse C. Cresswell, Gabriel Loaiza-Ganem

CoRR, 2022

Neural Implicit Manifold Learning for Topology-Aware Generative Modelling.

Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell

CoRR, 2022

Denoising Deep Generative Models.

Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini

Proceedings of the Proceedings on "I Can't Believe It's Not Better!, 2022

2021

Variational inference with continuously-indexed normalizing flows.

Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

Rectangular Flows for Manifold Learning.

Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham

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.

Panteha Naderian, Gabriel Loaiza-Ganem, Harry J. Braviner, Anthony L. Caterini, Jesse C. Cresswell, Tong Li, Animesh Garg

Proceedings of the 9th International Conference on Learning Representations, 2021

Entropic Issues in Likelihood-Based OOD Detection.

Anthony L. Caterini, Gabriel Loaiza-Ganem

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.

Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet

Proceedings of the 37th International Conference on Machine Learning, 2020

2019

Detecting anthropogenic cloud perturbations with deep learning.

Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier

CoRR, 2019

Localised Generative Flows.

Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet

CoRR, 2019

2018

Deep Neural Networks in a Mathematical Framework

Anthony L. Caterini, Dong Eui Chang

Springer Briefs in Computer Science, Springer, ISBN: 978-3-319-75303-4, 2018

Hamiltonian Variational Auto-Encoder.

Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic

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.

Anthony L. Caterini, Dong Eui Chang

CoRR, 2016

A Geometric Framework for Convolutional Neural Networks.

Anthony L. Caterini, Dong Eui Chang

CoRR, 2016

2015

Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark.

Manda Winlaw, Michael B. Hynes, Anthony L. Caterini, Hans De Sterck

Proceedings of the 21st IEEE International Conference on Parallel and Distributed Systems, 2015