Zobrazeno 1 - 10
of 86
pro vyhledávání: '"Skreta, Marta"'
Autor:
Schleich, Philipp, Skreta, Marta, Kristensen, Lasse B., Vargas-Hernández, Rodrigo A., Aspuru-Guzik, Alán
The feasibility of variational quantum algorithms, the most popular correspondent of neural networks on noisy, near-term quantum hardware, is highly impacted by the circuit depth of the involved parametrized quantum circuits (PQCs). Higher depth incr
Externí odkaz:
http://arxiv.org/abs/2410.23940
Autor:
Cheng, Austin, Ser, Cher Tian, Skreta, Marta, Guzmán-Cordero, Andrés, Thiede, Luca, Burger, Andreas, Aldossary, Abdulrahman, Leong, Shi Xuan, Pablo-García, Sergio, Strieth-Kalthoff, Felix, Aspuru-Guzik, Alán
Publikováno v:
Faraday Discuss., 2024
Machine learning has been pervasively touching many fields of science. Chemistry and materials science are no exception. While machine learning has been making a great impact, it is still not reaching its full potential or maturity. In this perspecti
Externí odkaz:
http://arxiv.org/abs/2409.10304
Autor:
Wang, Haorui, Skreta, Marta, Ser, Cher-Tian, Gao, Wenhao, Kong, Lingkai, Strieth-Kalthoff, Felix, Duan, Chenru, Zhuang, Yuchen, Yu, Yue, Zhu, Yanqiao, Du, Yuanqi, Aspuru-Guzik, Alán, Neklyudov, Kirill, Zhang, Chao
Molecular discovery, when formulated as an optimization problem, presents significant computational challenges because optimization objectives can be non-differentiable. Evolutionary Algorithms (EAs), often used to optimize black-box objectives in mo
Externí odkaz:
http://arxiv.org/abs/2406.16976
Autor:
Kristiadi, Agustinus, Strieth-Kalthoff, Felix, Skreta, Marta, Poupart, Pascal, Aspuru-Guzik, Alán, Pleiss, Geoff
Automation is one of the cornerstones of contemporary material discovery. Bayesian optimization (BO) is an essential part of such workflows, enabling scientists to leverage prior domain knowledge into efficient exploration of a large molecular space.
Externí odkaz:
http://arxiv.org/abs/2402.05015
Autor:
Skreta, Marta, Zhou, Zihan, Yuan, Jia Lin, Darvish, Kourosh, Aspuru-Guzik, Alán, Garg, Animesh
Advancements in large language models (LLMs) have demonstrated their potential in facilitating high-level reasoning, logical reasoning and robotics planning. Recently, LLMs have also been able to generate reward functions for low-level robot actions,
Externí odkaz:
http://arxiv.org/abs/2401.04157
Autor:
Skreta, Marta, Yoshikawa, Naruki, Arellano-Rubach, Sebastian, Ji, Zhi, Kristensen, Lasse Bjørn, Darvish, Kourosh, Aspuru-Guzik, Alán, Shkurti, Florian, Garg, Animesh
Generating low-level robot task plans from high-level natural language instructions remains a challenging problem. Although large language models have shown promising results in generating plans, the accuracy of the output remains unverified. Further
Externí odkaz:
http://arxiv.org/abs/2303.14100
Autor:
Krenn, Mario, Ai, Qianxiang, Barthel, Senja, Carson, Nessa, Frei, Angelo, Frey, Nathan C., Friederich, Pascal, Gaudin, Théophile, Gayle, Alberto Alexander, Jablonka, Kevin Maik, Lameiro, Rafael F., Lemm, Dominik, Lo, Alston, Moosavi, Seyed Mohamad, Nápoles-Duarte, José Manuel, Nigam, AkshatKumar, Pollice, Robert, Rajan, Kohulan, Schatzschneider, Ulrich, Schwaller, Philippe, Skreta, Marta, Smit, Berend, Strieth-Kalthoff, Felix, Sun, Chong, Tom, Gary, von Rudorff, Guido Falk, Wang, Andrew, White, Andrew, Young, Adamo, Yu, Rose, Aspuru-Guzik, Alán
Publikováno v:
Cell Patterns 3(10), 100588(2022)
Artificial intelligence (AI) and machine learning (ML) are expanding in popularity for broad applications to challenging tasks in chemistry and materials science. Examples include the prediction of properties, the discovery of new reaction pathways,
Externí odkaz:
http://arxiv.org/abs/2204.00056
Autor:
Kantor, Charles A., Skreta, Marta, Rauby, Brice, Boussioux, Léonard, Jehanno, Emmanuel, Luccioni, Alexandra, Rolnick, David, Talbot, Hugues
Publikováno v:
Proc. IJCAI 2021, Workshop on AI for Social Good, Harvard University (2021)
Fine-grained classification aims at distinguishing between items with similar global perception and patterns, but that differ by minute details. Our primary challenges come from both small inter-class variations and large intra-class variations. In t
Externí odkaz:
http://arxiv.org/abs/2103.11285
Abbreviation disambiguation is important for automated clinical note processing due to the frequent use of abbreviations in clinical settings. Current models for automated abbreviation disambiguation are restricted by the scarcity and imbalance of la
Externí odkaz:
http://arxiv.org/abs/1912.06174
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