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Alpha A. Lee
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2020 – today
- 2024
- [i13]Janosh Riebesell, T. Wesley Surta, Rhys E. A. Goodall, Michael Gaultois, Alpha A. Lee:
Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials. CoRR abs/2401.05848 (2024) - 2023
- [c1]Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Yuanqi Du, Samuel Stanton, Gary Tom, Bojana Rankovic, Arian Rokkum Jamasb, Aryan Deshwal, Julius Schwartz, Austin Tripp, Gregory Kell, Simon Frieder, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Johannes Peter Dürholt, Saudamini Chaurasia, Ji Won Park, Felix Strieth-Kalthoff, Alpha A. Lee, Bingqing Cheng, Alán Aspuru-Guzik, Philippe Schwaller, Jian Tang:
GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS 2023 - [i12]Janosh Riebesell, Rhys E. A. Goodall, Anubhav Jain, Philipp Benner, Kristin A. Persson, Alpha A. Lee:
Matbench Discovery - An evaluation framework for machine learning crystal stability prediction. CoRR abs/2308.14920 (2023) - 2022
- [j4]Ryan-Rhys Griffiths
, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee:
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. Mach. Learn. Sci. Technol. 3(1): 15004 (2022) - [i11]Ryan-Rhys Griffiths
GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR abs/2212.04450 (2022) - 2021
- [i10]Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee:
Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design. CoRR abs/2105.02637 (2021) - 2020
- [j3]Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction. J. Comput. Aided Mol. Des. 34(7): 717-730 (2020) - [j2]Todd J. Wills
Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes. J. Chem. Inf. Model. 60(10): 4449-4456 (2020) - [j1]Sathya R. Chitturi
Perspective: new insights from loss function landscapes of neural networks. Mach. Learn. Sci. Technol. 1(2): 23002 (2020) - [i9]William McCorkindale, Carl Poelking, Alpha A. Lee:
Investigating 3D Atomic Environments for Enhanced QSAR. CoRR abs/2010.12857 (2020) - [i8]Penelope Jones, Fabian Coupette, Andreas Härtel, Alpha A. Lee:
Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes. CoRR abs/2012.10694 (2020)
2010 – 2019
- 2019
- [i7]Yao Zhang, Alpha A. Lee:
Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. CoRR abs/1902.00925 (2019) - [i6]Matthew C. Robinson, Robert C. Glen, Alpha A. Lee:
Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction. CoRR abs/1905.11681 (2019) - [i5]Rhys E. A. Goodall, Alpha A. Lee:
Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. CoRR abs/1910.00617 (2019) - [i4]Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A. Aldrick, Alpha A. Lee:
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation. CoRR abs/1910.07779 (2019) - 2018
- [i3]Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee:
Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. CoRR abs/1803.01927 (2018) - [i2]Simon Becker, Yao Zhang, Alpha A. Lee:
Geometry of energy landscapes and the optimizability of deep neural networks. CoRR abs/1808.00408 (2018) - [i1]Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee:
Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation. CoRR abs/1811.02633 (2018)
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last updated on 2024-10-07 21:17 CEST by the dblp team
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