Anthony Caterini
Anthony Caterini
Machine Learning Scientist, Layer6 AI
Verified email at
Cited by
Cited by
Deep Neural Networks in a Mathematical Framework
AL Caterini, DE Chang
Springer International Publishing, 2018
Relaxing bijectivity constraints with continuously indexed normalising flows
R Cornish, A Caterini, G Deligiannidis, A Doucet
International conference on machine learning, 2133-2143, 2020
Hamiltonian variational auto-encoder
AL Caterini, A Doucet, D Sejdinovic
Advances in Neural Information Processing Systems 31, 2018
Algorithmic acceleration of parallel ALS for collaborative filtering: Speeding up distributed big data recommendation in spark
M Winlaw, MB Hynes, A Caterini, H De Sterck
2015 IEEE 21st International Conference on Parallel and Distributed Systems …, 2015
Verifying the union of manifolds hypothesis for image data
BCA Brown, AL Caterini, BL Ross, JC Cresswell, G Loaiza-Ganem
arXiv preprint arXiv:2207.02862, 2022
Rectangular flows for manifold learning
AL Caterini, G Loaiza-Ganem, G Pleiss, JP Cunningham
Advances in Neural Information Processing Systems 34, 30228-30241, 2021
Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
G Stein, J Cresswell, R Hosseinzadeh, Y Sui, B Ross, V Villecroze, Z Liu, ...
Advances in Neural Information Processing Systems 36, 2024
CaloMan: Fast generation of calorimeter showers with density estimation on learned manifolds
JC Cresswell, BL Ross, G Loaiza-Ganem, H Reyes-Gonzalez, M Letizia, ...
arXiv preprint arXiv:2211.15380, 2022
Diagnosing and fixing manifold overfitting in deep generative models
G Loaiza-Ganem, BL Ross, JC Cresswell, AL Caterini
arXiv preprint arXiv:2204.07172, 2022
Variational inference with continuously-indexed normalizing flows
A Caterini, R Cornish, D Sejdinovic, A Doucet
Uncertainty in Artificial Intelligence, 44-53, 2021
Entropic issues in likelihood-based ood detection
AL Caterini, G Loaiza-Ganem
I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 21-26, 2022
A Novel Mathematical Framework for the Analysis of Neural Networks
A Caterini
University of Waterloo, 2017
Neural Implicit Manifold Learning for Topology-Aware Density Estimation
BL Ross, G Loaiza-Ganem, AL Caterini, JC Cresswell
Transactions on Machine Learning Research, 2023
Denoising deep generative models
G Loaiza-Ganem, BL Ross, L Wu, JP Cunningham, JC Cresswell, ...
Proceedings on, 41-50, 2023
In-Context Data Distillation with TabPFN
J Ma, V Thomas, G Yu, A Caterini
arXiv preprint arXiv:2402.06971, 2024
TabPFGen--Tabular Data Generation with TabPFN
J Ma, A Dankar, G Stein, G Yu, A Caterini
arXiv preprint arXiv:2406.05216, 2024
Relating regularization and generalization through the intrinsic dimension of activations
BCA Brown, J Juravsky, AL Caterini, G Loaiza-Ganem
arXiv preprint arXiv:2211.13239, 2022
Deep Generative Models through the Lens of the Manifold Hypothesis: A Survey and New Connections
G Loaiza-Ganem, BL Ross, R Hosseinzadeh, AL Caterini, JC Cresswell
arXiv preprint arXiv:2404.02954, 2024
Lossless compression using continuously-indexed normalizing flows
A Golinski, AL Caterini
Neural Compression: From Information Theory to Applications--Workshop@ ICLR 2021, 2021
Detecting anthropogenic cloud perturbations with deep learning
D Watson-Parris, S Sutherland, M Christensen, A Caterini, D Sejdinovic, ...
The system can't perform the operation now. Try again later.
Articles 1–20