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ORCIDAnthony L. Caterini
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2020 – today
- 2023
[c9]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- [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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last updated on 2023-09-08 10:02 CEST by the dblp team
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