Alpha A. Lee - RankMe

18 papers A* 1Journal 17

Year	Rank	Type	Title / Venue / Authors
2024	J	`jnl`	Pushing the Pareto front of band gap and permittivity: ML-guided search for dielectric materials. CoRR Janosh Riebesell, T. Wesley Surta, Rhys E. A. Goodall, Michael Gaultois, Alpha A. Lee
2023	A*	`conf`	GAUCHE: A Library for Gaussian Processes in Chemistry. NeurIPS 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
2023	J	`jnl`	Matbench Discovery - An evaluation framework for machine learning crystal stability prediction. CoRR Janosh Riebesell, Rhys E. A. Goodall, Anubhav Jain, Philipp Benner, Kristin A. Persson, Alpha A. Lee
2022	J	`jnl`	Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation. Mach. Learn. Sci. Technol. Ryan-Rhys Griffiths, Alexander A. Aldrick, Miguel Garcia-Ortegon, Vidhi Lalchand, Alpha A. Lee
2022	J	`jnl`	GAUCHE: A Library for Gaussian Processes in Chemistry. CoRR Ryan-Rhys Griffiths, Leo Klarner, Henry B. Moss, Aditya Ravuri, Sang Truong, Bojana Rankovic, Yuanqi Du, Arian R. Jamasb, Julius Schwartz, Austin Tripp, Gregory Kell, Anthony Bourached, Alex Chan, Jacob Moss, Chengzhi Guo, Alpha A. Lee, Philippe Schwaller, Jian Tang
2021	J	`jnl`	Dataset Bias in the Natural Sciences: A Case Study in Chemical Reaction Prediction and Synthesis Design. CoRR Ryan-Rhys Griffiths, Philippe Schwaller, Alpha A. Lee
2020	J	`jnl`	Bayesian unsupervised learning reveals hidden structure in concentrated electrolytes. CoRR Penelope Jones, Fabian Coupette, Andreas Härtel, Alpha A. Lee
2020	J	`jnl`	Impact of Chemist-In-The-Loop Molecular Representations on Machine Learning Outcomes. J. Chem. Inf. Model. Todd J. Wills, Dmitrii A. Polshakov, Matthew C. Robinson, Alpha A. Lee
2020	J	`jnl`	Investigating 3D Atomic Environments for Enhanced QSAR. CoRR William McCorkindale, Carl Poelking, Alpha A. Lee
2020	J	`jnl`	Perspective: new insights from loss function landscapes of neural networks. Mach. Learn. Sci. Technol. Sathya R. Chitturi, Philipp C. Verpoort, Alpha A. Lee, David J. Wales
2020	J	`jnl`	Validating the validation: reanalyzing a large-scale comparison of deep learning and machine learning models for bioactivity prediction. J. Comput. Aided Mol. Des. Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
2019	J	`jnl`	Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation. CoRR Ryan-Rhys Griffiths, Miguel Garcia-Ortegon, Alexander A. Aldrick, Alpha A. Lee
2019	J	`jnl`	Bayesian semi-supervised learning for uncertainty-calibrated prediction of molecular properties and active learning. CoRR Yao Zhang, Alpha A. Lee
2019	J	`jnl`	Predicting materials properties without crystal structure: Deep representation learning from stoichiometry. CoRR Rhys E. A. Goodall, Alpha A. Lee
2019	J	`jnl`	Validating the Validation: Reanalyzing a large-scale comparison of Deep Learning and Machine Learning models for bioactivity prediction. CoRR Matthew C. Robinson, Robert C. Glen, Alpha A. Lee
2018	J	`jnl`	Energy-entropy competition and the effectiveness of stochastic gradient descent in machine learning. CoRR Yao Zhang, Andrew M. Saxe, Madhu S. Advani, Alpha A. Lee
2018	J	`jnl`	Geometry of energy landscapes and the optimizability of deep neural networks. CoRR Simon Becker, Yao Zhang, Alpha A. Lee
2018	J	`jnl`	Molecular Transformer for Chemical Reaction Prediction and Uncertainty Estimation. CoRR Philippe Schwaller, Teodoro Laino, Théophile Gaudin, Peter Bolgar, Costas Bekas, Alpha A. Lee