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2020 – today
- 2024
[i14]Sherry Yang, Simon L. Batzner, Ruiqi Gao, Muratahan Aykol, Alexander L. Gaunt, Brendan McMorrow, Danilo J. Rezende, Dale Schuurmans, Igor Mordatch, Ekin D. Cubuk:
Generative Hierarchical Materials Search. CoRR abs/2409.06762 (2024)- 2023
- [c14]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:
Project Silica: Towards Sustainable Cloud Archival Storage in Glass. SOSP 2023: 166-181 - 2022
- [c13]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Simple GNN Regularisation for 3D Molecular Property Prediction and Beyond. ICLR 2022 - [i13]Michael Schaarschmidt, Morgane Riviere, Alex M. Ganose, James S. Spencer, Alexander L. Gaunt, James Kirkpatrick, Simon Axelrod, Peter W. Battaglia, Jonathan Godwin:
Learned Force Fields Are Ready For Ground State Catalyst Discovery. CoRR abs/2209.12466 (2022) - 2021
- [i12]Jonathan Godwin, Michael Schaarschmidt, Alexander L. Gaunt, Alvaro Sanchez-Gonzalez, Yulia Rubanova, Petar Velickovic, James Kirkpatrick, Peter W. Battaglia:
Very Deep Graph Neural Networks Via Noise Regularisation. CoRR abs/2106.07971 (2021)
2010 – 2019
- 2019
- [c12]Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov:
Generative Code Modeling with Graphs. ICLR (Poster) 2019 - [c11]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Deterministic Variational Inference for Robust Bayesian Neural Networks. ICLR 2019 - [c10]Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Learning to Represent Edits. ICLR (Poster) 2019 - 2018
- [c9]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:
Glass: A New Media for a New Era? HotStorage 2018 - [c8]Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel:
Graph Partition Neural Networks for Semi-Supervised Classification. ICLR (Workshop) 2018 - [c7]Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Constrained Graph Variational Autoencoders for Molecule Design. NeurIPS 2018: 7806-7815 - [i11]Renjie Liao, Marc Brockschmidt, Daniel Tarlow, Alexander L. Gaunt, Raquel Urtasun, Richard S. Zemel:
Graph Partition Neural Networks for Semi-Supervised Classification. CoRR abs/1803.06272 (2018) - [i10]Marc Brockschmidt, Miltiadis Allamanis, Alexander L. Gaunt, Oleksandr Polozov:
Generative Code Modeling with Graphs. CoRR abs/1805.08490 (2018) - [i9]Qi Liu, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Constrained Graph Variational Autoencoders for Molecule Design. CoRR abs/1805.09076 (2018) - [i8]Anqi Wu, Sebastian Nowozin, Edward Meeds, Richard E. Turner, José Miguel Hernández-Lobato, Alexander L. Gaunt:
Fixing Variational Bayes: Deterministic Variational Inference for Bayesian Neural Networks. CoRR abs/1810.03958 (2018) - [i7]Pengcheng Yin, Graham Neubig, Miltiadis Allamanis, Marc Brockschmidt, Alexander L. Gaunt:
Learning to Represent Edits. CoRR abs/1810.13337 (2018) - 2017
- [c6]Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow:
DeepCoder: Learning to Write Programs. ICLR (Poster) 2017 - [c5]John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow:
Neural Functional Programming. ICLR (Workshop) 2017 - [c4]Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow:
Lifelong Perceptual Programming By Example. ICLR (Workshop) 2017 - [c3]Chengtao Li, Daniel Tarlow, Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman:
Neural Program Lattices. ICLR (Poster) 2017 - [c2]Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow:
Differentiable Programs with Neural Libraries. ICML 2017: 1213-1222 - [i6]Alex Gaunt, Matthew Johnson, Maik Riechert, Daniel Tarlow, Ryota Tomioka, Dimitrios Vytiniotis, Sam Webster:
AMPNet: Asynchronous Model-Parallel Training for Dynamic Neural Networks. CoRR abs/1705.09786 (2017) - 2016
- [c1]Alex Gaunt, Diana Borsa, Yoram Bachrach:
Training Neural Nets to Aggregate Crowdsourced Responses. UAI 2016 - [i5]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1608.04428 (2016) - [i4]John K. Feser, Marc Brockschmidt, Alexander L. Gaunt, Daniel Tarlow:
Neural Functional Programming. CoRR abs/1611.01988 (2016) - [i3]Matej Balog, Alexander L. Gaunt, Marc Brockschmidt, Sebastian Nowozin, Daniel Tarlow:
DeepCoder: Learning to Write Programs. CoRR abs/1611.01989 (2016) - [i2]Alexander L. Gaunt, Marc Brockschmidt, Nate Kushman, Daniel Tarlow:
Lifelong Perceptual Programming By Example. CoRR abs/1611.02109 (2016) - [i1]Alexander L. Gaunt, Marc Brockschmidt, Rishabh Singh, Nate Kushman, Pushmeet Kohli, Jonathan Taylor, Daniel Tarlow:
Summary - TerpreT: A Probabilistic Programming Language for Program Induction. CoRR abs/1612.00817 (2016)
Coauthor Index
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last updated on 2024-10-23 20:33 CEST by the dblp team
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