Mario Krenn

According to our database, Mario Krenn authored at least 23 papers between 2017 and 2024.

Collaborative distances:

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

2024

Deep quantum graph dreaming: deciphering neural network insights into quantum experiments.

Tareq Jaouni, Sören Arlt, Carlos Ruiz-Gonzalez, Ebrahim Karimi, Xuemei Gu, Mario Krenn

Mach. Learn. Sci. Technol., March, 2024

Virtual Reality for Understanding Artificial-Intelligence-driven Scientific Discovery with an Application in Quantum Optics.

Philipp Schmidt, Sören Arlt, Carlos Ruiz-Gonzalez, Xuemei Gu, Carla Rodríguez, Mario Krenn

CoRR, 2024

Forecasting high-impact research topics via machine learning on evolving knowledge graphs.

Xuemei Gu, Mario Krenn

CoRR, 2024

2023

Digital Discovery of 100 diverse Quantum Experiments with PyTheus.

Carlos Ruiz-Gonzalez, Sören Arlt, Jan Petermann, Sharareh Sayyad, Tareq Jaouni, Ebrahim Karimi, Nora Tischler, Xuemei Gu, Mario Krenn

Quantum, December, 2023

Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network.

Mario Krenn, Lorenzo Buffoni, Bruno C. Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, João P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp

Nat. Mac. Intell., October, 2023

Recent advances in the Self-Referencing Embedding Strings (SELFIES) library.

Alston Lo, Robert Pollice, AkshatKumar Nigam, Andrew D. White, Mario Krenn, Alán Aspuru-Guzik

CoRR, 2023

2022

Design of quantum optical experiments with logic artificial intelligence.

Alba Cervera-Lierta, Mario Krenn, Alán Aspuru-Guzik

Quantum, September, 2022

SELFIES and the future of molecular string representations.

Mario Krenn, Qianxiang Ai, Senja Barthel, Nessa Carson, Angelo Frei, Nathan C. Frey, Pascal Friederich, Théophile Gaudin, Alberto Alexander Gayle, Kevin Maik Jablonka, Rafael F. Lameiro, Dominik Lemm, Alston Lo, Seyed Mohamad Moosavi, José Manuel Nápoles-Duarte, AkshatKumar Nigam, Robert Pollice, Kohulan Rajan, Ulrich Schatzschneider, Philippe Schwaller, Marta Skreta, Berend Smit, Felix Strieth-Kalthoff, Chong Sun, Gary Tom, Guido Falk von Rudorff, Andrew Wang, Andrew D. White, Adamo Young, Rose Yu, Alán Aspuru-Guzik

Patterns, 2022

Learning interpretable representations of entanglement in quantum optics experiments using deep generative models.

Daniel Flam-Shepherd, Tony C. Wu, Xuemei Gu, Alba Cervera-Lierta, Mario Krenn, Alán Aspuru-Guzik

Nat. Mach. Intell., 2022

Curiosity in exploring chemical spaces: intrinsic rewards for molecular reinforcement learning.

Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2022

Predicting the Future of AI with AI: High-quality link prediction in an exponentially growing knowledge network.

Mario Krenn, Lorenzo Buffoni, Bruno C. Coutinho, Sagi Eppel, Jacob Gates Foster, Andrew Gritsevskiy, Harlin Lee, Yichao Lu, João P. Moutinho, Nima Sanjabi, Rishi Sonthalia, Ngoc Mai Tran, Francisco Valente, Yangxinyu Xie, Rose Yu, Michael Kopp

CoRR, 2022

Artificial Intelligence and Machine Learning for Quantum Technologies.

Mario Krenn, Jonas Landgraf, Thomas Foesel, Florian Marquardt

CoRR, 2022

On scientific understanding with artificial intelligence.

Mario Krenn, Robert Pollice, Si Yue Guo, Matteo Aldeghi, Alba Cervera-Lierta, Pascal Friederich, Gabriel dos Passos Gomes, Florian Häse, Adrian Jinich, AkshatKumar Nigam, Zhenpeng Yao, Alán Aspuru-Guzik

CoRR, 2022

2021

Deep molecular dreaming: inverse machine learning for de-novo molecular design and interpretability with surjective representations.

Cynthia Shen, Mario Krenn, Sagi Eppel, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

Scientific intuition inspired by machine learning-generated hypotheses.

Pascal Friederich, Mario Krenn, Isaac Tamblyn, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2021

2020

Self-referencing embedded strings (SELFIES): A 100% robust molecular string representation.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

Mach. Learn. Sci. Technol., 2020

Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning.

Luca A. Thiede, Mario Krenn, AkshatKumar Nigam, Alán Aspuru-Guzik

CoRR, 2020

Computer-inspired Quantum Experiments.

Mario Krenn, Manuel Erhard, Anton Zeilinger

CoRR, 2020

Augmenting Genetic Algorithms with Deep Neural Networks for Exploring the Chemical Space.

AkshatKumar Nigam, Pascal Friederich, Mario Krenn, Alán Aspuru-Guzik

Proceedings of the 8th International Conference on Learning Representations, 2020

2019

Quantum Optical Experiments Modeled by Long Short-Term Memory.

Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter

CoRR, 2019

Predicting Research Trends with Semantic and Neural Networks with an application in Quantum Physics.

Mario Krenn, Anton Zeilinger

CoRR, 2019

SELFIES: a robust representation of semantically constrained graphs with an example application in chemistry.

Mario Krenn, Florian Häse, AkshatKumar Nigam, Pascal Friederich, Alán Aspuru-Guzik

CoRR, 2019

2017

Active learning machine learns to create new quantum experiments.

Alexey A. Melnikov, Hendrik Poulsen Nautrup, Mario Krenn, Vedran Dunjko, Markus Tiersch, Anton Zeilinger, Hans J. Briegel

CoRR, 2017