Anthony L. Caterini

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 34

Top Topics

Manifold Learning

Convolutional Neural Networks

Intrinsic Dimension

Data Generation

Top Venues

Trans. Mach. Learn. Res.

Publications

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 (2024)
Junwei Ma, Valentin Thomas, Guangwei Yu, Anthony L. Caterini
In-Context Data Distillation with TabPFN. CoRR (2024)
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)
Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, Anthony L. Caterini
TabPFGen - Tabular Data Generation with TabPFN. CoRR (2024)
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 (2024)
Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini
Retrieval & Fine-Tuning for In-Context Tabular Models. CoRR (2024)
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)
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)
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 (2023)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR (2022)
Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR (2022)
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 (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. CoRR (2022)
Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem
Relating Regularization and Generalization through the Intrinsic Dimension of Activations. CoRR (2022)
Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini
Denoising Deep Generative Models. ICBINB (2022)
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)
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 (2022)
Anthony L. Caterini, Gabriel Loaiza-Ganem
Entropic Issues in Likelihood-Based OOD Detection. CoRR (2021)
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)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. CoRR (2021)
Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
Variational inference with continuously-indexed normalizing flows. UAI (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham
Rectangular Flows for Manifold Learning. NeurIPS (2021)
Anthony L. Caterini, Gabriel Loaiza-Ganem
Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS (2021)
Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. ICML (2020)
Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR (2020)
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 (2020)
Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet
Localised Generative Flows. CoRR (2019)
Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier
Detecting anthropogenic cloud perturbations with deep learning. CoRR (2019)
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Hamiltonian Variational Auto-Encoder. CoRR (2018)
Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic
Hamiltonian Variational Auto-Encoder. NeurIPS (2018)
Anthony L. Caterini, Dong Eui Chang
A Geometric Framework for Convolutional Neural Networks. CoRR (2016)
Anthony L. Caterini, Dong Eui Chang
A Novel Representation of Neural Networks. CoRR (2016)
Manda Winlaw, Michael B. Hynes, Anthony L. Caterini, Hans De Sterck
Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark. ICPADS (2015)
Manda Winlaw, Michael B. Hynes, Anthony L. Caterini, Hans De Sterck
Algorithmic Acceleration of Parallel ALS for Collaborative Filtering: Speeding up Distributed Big Data Recommendation in Spark. CoRR (2015)