Alexander L. Gaunt

According to our database, Alexander L. Gaunt authored at least 20 papers between 2016 and 2023.

Collaborative distances:

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

2023

Project Silica: Towards Sustainable Cloud Archival Storage in Glass.

Patrick Anderson, Erika Blancada Aranas, Youssef Assaf, Raphael Behrendt, Richard Black, Marco Caballero, Pashmina Cameron, Burcu Canakci, Thales De Carvalho, Andromachi Chatzieleftheriou, Rebekah Storan Clarke, James Clegg, Daniel Cletheroe, Bridgette Cooper, Tim Deegan, Austin Donnelly, Rokas Drevinskas, Alexander L. Gaunt, Christos Gkantsidis, Ariel Gomez Diaz, István Haller, Freddie Hong, Teodora Ilieva, Shashidhar Joshi, Russell Joyce, Mint Kunkel, David Lara, Sergey Legtchenko, Fanglin Linda Liu, Bruno Magalhães, Alana Marzoev, Marvin McNett, Jayashree Mohan, Michael Myrah, Trong Nguyen, Sebastian Nowozin, Aaron Ogus, Hiske Overweg, Antony I. T. Rowstron, Maneesh Sah, Masaaki Sakakura, Peter Scholtz, Nina Schreiner, Omer Sella, Adam Smith, Ioan A. Stefanovici, David Sweeney, Benn Thomsen, Govert Verkes, Phil Wainman, Jonathan Westcott, Luke Weston, Charles Whittaker, Pablo Wilke Berenguer, Hugh Williams, Thomas Winkler, Stefan Winzeck

Proceedings of the 29th Symposium on Operating Systems Principles, 2023

2022

Learned Force Fields Are Ready For Ground State Catalyst Discovery.

Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin

CoRR, 2022

Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

Very Deep Graph Neural Networks Via Noise Regularisation.

Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia

CoRR, 2021

2019

Learning to Represent Edits.

Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

Proceedings of the 7th International Conference on Learning Representations, 2019

Deterministic Variational Inference for Robust Bayesian Neural Networks.

Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

Proceedings of the 7th International Conference on Learning Representations, 2019

Generative Code Modeling with Graphs.

Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov

Proceedings of the 7th International Conference on Learning Representations, 2019

2018

Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks.

Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt

CoRR, 2018

Constrained Graph Variational Autoencoders for Molecule Design.

Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Graph Partition Neural Networks for Semi-Supervised Classification.

Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel

Proceedings of the 6th International Conference on Learning Representations, 2018

Glass: A New Media for a New Era?

Patrick Anderson, Richard Black, Ausra Cerkauskaite, Andromachi Chatzieleftheriou, James Clegg, Chris Dainty, Raluca Diaconu, Rokas Drevinskas, Austin Donnelly, Alexander L. Gaunt, Andreas Georgiou, Ariel Gomez Diaz, Peter G. Kazansky, David Lara, Sergey Legtchenko, Sebastian Nowozin, Aaron Ogus, Douglas Phillips, Antony I. T. Rowstron, Masaaki Sakakura, Ioan A. Stefanovici, Benn Thomsen, Lei Wang, Hugh Williams, Mengyang Yang

Proceedings of the 10th USENIX Workshop on Hot Topics in Storage and File Systems, 2018

2017

AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks.

Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster

CoRR, 2017

Differentiable Programs with Neural Libraries.

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

Proceedings of the 34th International Conference on Machine Learning, 2017

Neural Program Lattices.

Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman

Proceedings of the 5th International Conference on Learning Representations, 2017

Lifelong Perceptual Programming By Example.

Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

Neural Functional Programming.

John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

DeepCoder: Learning to Write Programs.

Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow

Proceedings of the 5th International Conference on Learning Representations, 2017

2016

Summary - TerpreT: A Probabilistic Programming Language for Program Induction.

Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

CoRR, 2016

TerpreT: A Probabilistic Programming Language for Program Induction.

Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow

CoRR, 2016

Training Neural Nets to Aggregate Crowdsourced Responses.

Alex Gaunt, Diana Borsa, Yoram Bachrach

Proceedings of the Thirty-Second Conference on Uncertainty in Artificial Intelligence, 2016