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ORCIDAnthony L. Caterini
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2020 – today
- 2024
[j2]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)- [c12]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. ICML 2024 - [c11]Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini:
Retrieval & Fine-Tuning for In-Context Tabular Models. NeurIPS 2024 - [i24]Junwei Ma, Valentin Thomas, Guangwei Yu, Anthony L. Caterini:
In-Context Data Distillation with TabPFN. CoRR abs/2402.06971 (2024) - [i23]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 abs/2403.18910 (2024) - [i22]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 abs/2404.02954 (2024) - [i21]Valentin Thomas, Junwei Ma, Rasa Hosseinzadeh, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini:
Retrieval & Fine-Tuning for In-Context Tabular Models. CoRR abs/2406.05207 (2024) - [i20]Junwei Ma, Apoorv Dankar, George Stein, Guangwei Yu, Anthony L. Caterini:
TabPFGen - Tabular Data Generation with TabPFN. CoRR abs/2406.05216 (2024) - [i19]Junwei Ma, Valentin Thomas, Rasa Hosseinzadeh, Hamidreza Kamkari, Alex Labach, Jesse C. Cresswell, Keyvan Golestan, Guangwei Yu, Maksims Volkovs, Anthony L. Caterini:
TabDPT: Scaling Tabular Foundation Models. CoRR abs/2410.18164 (2024) - [i18]Claudius Krause, Michele Faucci Giannelli, Gregor Kasieczka, Benjamin Nachman, Dalila Salamani, David Shih, Anna Zaborowska, Oz Amram, Kerstin Borras, Matthew R. Buckley, Erik Buhmann, Thorsten Buss, Renato Paulo Da Costa Cardoso, Anthony L. Caterini, Nadezda Chernyavskaya, Federico A. G. Corchia
, Jesse C. Cresswell, Sascha Diefenbacher, Etienne Dreyer, Vijay Ekambaram, Engin Eren, Florian Ernst, Luigi Favaro, Matteo Franchini, Frank Gaede, Eilam Gross, Shih-Chieh Hsu, Kristina Jaruskova, Benno Käch, Jayant Kalagnanam, Raghav Kansal, Taewoo Kim, Dmitrii Kobylianskii, Anatolii Korol, William Korcari, Dirk Krücker, Katja Krüger, Marco Letizia, Shu Li, Qibin Liu, Xiulong Liu, Gabriel Loaiza-Ganem, Thandikire Madula, Peter McKeown, Isabell-A. Melzer-Pellmann, Vinicius Mikuni, Nam Nguyen, Ayodele Ore, Sofia Palacios Schweitzer, Ian Pang, Kevin Pedro, Tilman Plehn, Witold Pokorski, Huilin Qu, Piyush Raikwar, John A. Raine, Humberto Reyes-González, Lorenzo Rinaldi, Brendan Leigh Ross, Moritz A. W. Scham, Simon Schnake, Chase Shimmin, Eli Shlizerman, Nathalie Soybelman, Mudhakar Srivatsa, Kalliopi Tsolaki, Sofia Vallecorsa, Kyongmin Yeo, Rui Zhang:
CaloChallenge 2022: A Community Challenge for Fast Calorimeter Simulation. CoRR abs/2410.21611 (2024) - 2023
- [c10]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 - [c9]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 - [i17]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 abs/2306.04675 (2023) - 2022
- [j1]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) - [c8]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. ICBINB 2022: 41-50 - [i16]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Jesse C. Cresswell, Anthony L. Caterini:
Diagnosing and Fixing Manifold Overfitting in Deep Generative Models. CoRR abs/2204.07172 (2022) - [i15]Brendan Leigh Ross, Gabriel Loaiza-Ganem, Anthony L. Caterini, Jesse C. Cresswell:
Neural Implicit Manifold Learning for Topology-Aware Generative Modelling. CoRR abs/2206.11267 (2022) - [i14]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 abs/2207.02862 (2022) - [i13]Bradley C. A. Brown, Jordan Juravsky, Anthony L. Caterini, Gabriel Loaiza-Ganem:
Relating Regularization and Generalization through the Intrinsic Dimension of Activations. CoRR abs/2211.13239 (2022) - [i12]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 abs/2211.15380 (2022) - [i11]Gabriel Loaiza-Ganem, Brendan Leigh Ross, Luhuan Wu, John P. Cunningham, Jesse C. Cresswell, Anthony L. Caterini:
Denoising Deep Generative Models. CoRR abs/2212.01265 (2022) - 2021
- [c7]Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. ICBINB@NeurIPS 2021: 21-26 - [c6]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 - [c5]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. NeurIPS 2021: 30228-30241 - [c4]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational inference with continuously-indexed normalizing flows. UAI 2021: 44-53 - [i10]Anthony L. Caterini, Gabriel Loaiza-Ganem, Geoff Pleiss, John P. Cunningham:
Rectangular Flows for Manifold Learning. CoRR abs/2106.01413 (2021) - [i9]Anthony L. Caterini, Gabriel Loaiza-Ganem:
Entropic Issues in Likelihood-Based OOD Detection. CoRR abs/2109.10794 (2021) - 2020
- [c3]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Relaxing Bijectivity Constraints with Continuously Indexed Normalising Flows. ICML 2020: 2133-2143 - [i8]Anthony L. Caterini, Robert Cornish, Dino Sejdinovic, Arnaud Doucet:
Variational Inference with Continuously-Indexed Normalizing Flows. CoRR abs/2007.05426 (2020) - [i7]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 abs/2011.12363 (2020)
2010 – 2019
- 2019
- [i6]Robert Cornish, Anthony L. Caterini, George Deligiannidis, Arnaud Doucet:
Localised Generative Flows. CoRR abs/1909.13833 (2019) - [i5]Duncan Watson-Parris, Samuel Sutherland, Matthew Christensen, Anthony L. Caterini, Dino Sejdinovic, Philip Stier:
Detecting anthropogenic cloud perturbations with deep learning. CoRR abs/1911.13061 (2019) - 2018
- [b1]Anthony L. Caterini, Dong Eui Chang:
Deep Neural Networks in a Mathematical Framework. Springer Briefs in Computer Science, Springer 2018, ISBN 978-3-319-75303-4, pp. 1-82 - [c2]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. NeurIPS 2018: 8178-8188 - [i4]Anthony L. Caterini, Arnaud Doucet, Dino Sejdinovic:
Hamiltonian Variational Auto-Encoder. CoRR abs/1805.11328 (2018) - 2016
- [i3]Anthony L. Caterini, Dong Eui Chang:
A Geometric Framework for Convolutional Neural Networks. CoRR abs/1608.04374 (2016) - [i2]Anthony L. Caterini, Dong Eui Chang:
A Novel Representation of Neural Networks. CoRR abs/1610.01549 (2016) - 2015
- [c1]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: 682-691 - [i1]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 abs/1508.03110 (2015)
Coauthor Index
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