Zobrazeno 1 - 10
of 675
pro vyhledávání: '"Riley, Patrick A."'
Publikováno v:
Sci. Adv.8, eabq0279 (2022)
Systematic development of accurate density functionals has been a decades-long challenge for scientists. Despite the emerging application of machine learning (ML) in approximating functionals, the resulting ML functionals usually contain more than te
Externí odkaz:
http://arxiv.org/abs/2203.02540
Autor:
Renner, Julie A.1 (AUTHOR) Julie.a.renner.civ@army.mil, Riley, Patrick C.1 (AUTHOR)
Publikováno v:
Journal of Histotechnology. Apr2024, p1-4. 4p. 2 Illustrations.
Autor:
Li, Li, Hoyer, Stephan, Pederson, Ryan, Sun, Ruoxi, Cubuk, Ekin D., Riley, Patrick, Burke, Kieron
Publikováno v:
Phys. Rev. Lett. 126, 036401 (2021)
Including prior knowledge is important for effective machine learning models in physics, and is usually achieved by explicitly adding loss terms or constraints on model architectures. Prior knowledge embedded in the physics computation itself rarely
Externí odkaz:
http://arxiv.org/abs/2009.08551
Symbolic techniques based on Satisfiability Modulo Theory (SMT) solvers have been proposed for analyzing and verifying neural network properties, but their usage has been fairly limited owing to their poor scalability with larger networks. In this wo
Externí odkaz:
http://arxiv.org/abs/2006.16322
Autor:
McCloskey, Kevin, Sigel, Eric A., Kearnes, Steven, Xue, Ling, Tian, Xia, Moccia, Dennis, Gikunju, Diana, Bazzaz, Sana, Chan, Betty, Clark, Matthew A., Cuozzo, John W., Guié, Marie-Aude, Guilinger, John P., Huguet, Christelle, Hupp, Christopher D., Keefe, Anthony D., Mulhern, Christopher J., Zhang, Ying, Riley, Patrick
DNA-encoded small molecule libraries (DELs) have enabled discovery of novel inhibitors for many distinct protein targets of therapeutic value through screening of libraries with up to billions of unique small molecules. We demonstrate a new approach
Externí odkaz:
http://arxiv.org/abs/2002.02530
Publikováno v:
Phys. Rev. Research 2, 023074 (2020)
The Quantum Approximate Optimization Algorithm (QAOA) is a standard method for combinatorial optimization with a gate-based quantum computer. The QAOA consists of a particular ansatz for the quantum circuit architecture, together with a prescription
Externí odkaz:
http://arxiv.org/abs/1909.07621
We present RL-VAE, a graph-to-graph variational autoencoder that uses reinforcement learning to decode molecular graphs from latent embeddings. Methods have been described previously for graph-to-graph autoencoding, but these approaches require sophi
Externí odkaz:
http://arxiv.org/abs/1904.08915
Publikováno v:
NeurIPS 2019 Workshop on Knowledge Representation & Reasoning Meets Machine Learning
Symbolic regression is a type of discrete optimization problem that involves searching expressions that fit given data points. In many cases, other mathematical constraints about the unknown expression not only provide more information beyond just va
Externí odkaz:
http://arxiv.org/abs/1901.07714
Autor:
Yang, Lusann, Haber, Joel A., Armstrong, Zan, Yang, Samuel J., Kan, Kevin, Zhou, Lan, Richter, Matthias H., Roat, Christopher, Wagner, Nicholas, Coram, Marc, Berndl, Marc, Riley, Patrick, Gregoire, John M.
Publikováno v:
Proceedings of the National Academy of Sciences of the United States of America, 2021 Sep . 118(37), 1-10.
Externí odkaz:
https://www.jstor.org/stable/27075707
We present a framework, which we call Molecule Deep $Q$-Networks (MolDQN), for molecule optimization by combining domain knowledge of chemistry and state-of-the-art reinforcement learning techniques (double $Q$-learning and randomized value functions
Externí odkaz:
http://arxiv.org/abs/1810.08678