James Urquhart Allingham

According to our database, James Urquhart Allingham authored at least 12 papers between 2020 and 2024.

Collaborative distances:

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

2024

A Generative Model of Symmetry Transformations.

James Urquhart Allingham, Bruno Kacper Mlodozeniec, Shreyas Padhy, Javier Antorán, David Krueger, Richard E. Turner, Eric T. Nalisnick, José Miguel Hernández-Lobato

CoRR, 2024

2023

Towards Anytime Classification in Early-Exit Architectures by Enforcing Conditional Monotonicity.

Metod Jazbec, James Urquhart Allingham, Dan Zhang, Eric T. Nalisnick

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

A Simple Zero-shot Prompt Weighting Technique to Improve Prompt Ensembling in Text-Image Models.

James Urquhart Allingham, Jie Ren, Michael W. Dusenberry, Xiuye Gu, Yin Cui, Dustin Tran, Jeremiah Zhe Liu, Balaji Lakshminarayanan

Proceedings of the International Conference on Machine Learning, 2023

2022

Deep Classifiers with Label Noise Modeling and Distance Awareness.

Vincent Fortuin, Mark Collier, Florian Wenzel, James Urquhart Allingham, Jeremiah Zhe Liu, Dustin Tran, Balaji Lakshminarayanan, Jesse Berent, Rodolphe Jenatton, Effrosyni Kokiopoulou

Trans. Mach. Learn. Res., 2022

Sparse MoEs meet Efficient Ensembles.

James Urquhart Allingham, Florian Wenzel, Zelda E. Mariet, Basil Mustafa, Joan Puigcerver, Neil Houlsby, Ghassen Jerfel, Vincent Fortuin, Balaji Lakshminarayanan, Jasper Snoek, Dustin Tran, Carlos Riquelme Ruiz, Rodolphe Jenatton

Trans. Mach. Learn. Res., 2022

Adapting the Linearised Laplace Model Evidence for Modern Deep Learning.

Javier Antorán, David Janz, James Urquhart Allingham, Erik A. Daxberger, Riccardo Barbano, Eric T. Nalisnick, José Miguel Hernández-Lobato

Proceedings of the International Conference on Machine Learning, 2022

2021

Depth Uncertainty Networks for Active Learning.

Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2021

Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the 38th International Conference on Machine Learning, 2021

Addressing Bias in Active Learning with Depth Uncertainty Networks... or Not.

Chelsea Murray, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

Proceedings of the I (Still) Can't Believe It's Not Better! Workshop at NeurIPS 2021, 2021

2020

Expressive yet Tractable Bayesian Deep Learning via Subnetwork Inference.

Erik A. Daxberger, Eric T. Nalisnick, James Urquhart Allingham, Javier Antorán, José Miguel Hernández-Lobato

CoRR, 2020

Variational Depth Search in ResNets.

Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato

CoRR, 2020

Depth Uncertainty in Neural Networks.

Javier Antorán, James Urquhart Allingham, José Miguel Hernández-Lobato

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020