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pro vyhledávání: '"Williams , Mike"'
Autor:
Kitouni, Ouail, Nolte, Niklas, Pérez-Díaz, Víctor Samuel, Trifinopoulos, Sokratis, Williams, Mike
Mechanistic Interpretability (MI) promises a path toward fully understanding how neural networks make their predictions. Prior work demonstrates that even when trained to perform simple arithmetic, models can implement a variety of algorithms (someti
Externí odkaz:
http://arxiv.org/abs/2405.17425
Autor:
Delaney, Blaise, Schulte, Nicole, Ciezarek, Gregory, Nolte, Niklas, Williams, Mike, Albrecht, Johannes
The operating conditions defining the current data taking campaign at the Large Hadron Collider, known as Run 3, present unparalleled challenges for the real-time data acquisition workflow of the LHCb experiment at CERN. To address the anticipated su
Externí odkaz:
http://arxiv.org/abs/2312.14265
The Electron-Ion Collider~(EIC), a forthcoming powerful high-luminosity facility, represents an exciting opportunity to explore new physics. In this article, we study the potential of the EIC to probe the coupling between axion-like particles~(ALPs)
Externí odkaz:
http://arxiv.org/abs/2310.08827
Autor:
Schulte, Nicole, Delaney, Blaise Raheem, Nolte, Niklas, Ciezarek, Gregory Max, Albrecht, Johannes, Williams, Mike
The data-taking conditions expected in Run 3 of the LHCb experiment at CERN are unprecedented and challenging for the software and computing systems. Despite that, the LHCb collaboration pioneers the use of a software-only trigger system to cope with
Externí odkaz:
http://arxiv.org/abs/2306.09873
We introduce Nuclear Co-Learned Representations (NuCLR), a deep learning model that predicts various nuclear observables, including binding and decay energies, and nuclear charge radii. The model is trained using a multi-task approach with shared rep
Externí odkaz:
http://arxiv.org/abs/2306.06099
Autor:
Boveia, Antonio, Berkat, Mohamed, Chen, Thomas Y., Desai, Aman, Doglioni, Caterina, Drlica-Wagner, Alex, Gardner, Susan, Gori, Stefania, Greaves, Joshua, Harding, Patrick, Harris, Philip C., Lippincott, W. Hugh, Monzani, Maria Elena, Pachal, Katherine, Prescod-Weinstein, Chanda, Rybka, Gray, Shakya, Bibhushan, Shelton, Jessie, Slatyer, Tracy R., Steinhebel, Amanda, Tanedo, Philip, Toro, Natalia, Tsai, Yun-Tse, Williams, Mike, Winslow, Lindley, Yu, Jaehoon, Yu, Tien-Tien
The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all Frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of
Externí odkaz:
http://arxiv.org/abs/2211.07027
Autor:
Boveia, Antonio, Berkat, Mohamed, Chen, Thomas Y., Desai, Aman, Doglioni, Caterina, Drlica-Wagner, Alex, Gardner, Susan, Gori, Stefania, Greaves, Joshua, Harding, Patrick, Harris, Philip C., Lippincott, W. Hugh, Monzani, Maria Elena, Pachal, Katherine, Prescod-Weinstein, Chanda, Rybka, Gray, Shakya, Bibhushan, Shelton, Jessie, Slatyer, Tracy R., Steinhebel, Amanda, Tanedo, Philip, Toro, Natalia, Tsai, Yun-Tse, Williams, Mike, Winslow, Lindley, Yu, Jaehoon, Yu, Tien-Tien
The fundamental nature of Dark Matter is a central theme of the Snowmass 2021 process, extending across all frontiers. In the last decade, advances in detector technology, analysis techniques and theoretical modeling have enabled a new generation of
Externí odkaz:
http://arxiv.org/abs/2210.01770