Zobrazeno 1 - 10
of 13 765
pro vyhledávání: '"Brandon, M"'
Autor:
Peskoff, Denis, Visokay, Adam, Schulhoff, Sander, Wachspress, Benjamin, Blinder, Alan, Stewart, Brandon M.
Markets and policymakers around the world hang on the consequential monetary policy decisions made by the Federal Open Market Committee (FOMC). Publicly available textual documentation of their meetings provides insight into members' attitudes about
Externí odkaz:
http://arxiv.org/abs/2407.19110
Publikováno v:
ICML 2024
Crystalline materials are a fundamental component in next-generation technologies, yet modeling their distribution presents unique computational challenges. Of the plausible arrangements of atoms in a periodic lattice only a vanishingly small percent
Externí odkaz:
http://arxiv.org/abs/2406.04713
Autor:
Wongkamjan, Wichayaporn, Gu, Feng, Wang, Yanze, Hermjakob, Ulf, May, Jonathan, Stewart, Brandon M., Kummerfeld, Jonathan K., Peskoff, Denis, Boyd-Graber, Jordan Lee
The boardgame Diplomacy is a challenging setting for communicative and cooperative artificial intelligence. The most prominent communicative Diplomacy AI, Cicero, has excellent strategic abilities, exceeding human players. However, the best Diplomacy
Externí odkaz:
http://arxiv.org/abs/2406.04643
Autor:
Bhattacharya, Rajorshi, Medina, Brandon M., Pihlström, Ylva M., Sjouwerman, Loránt O., Lewis, Megan O., Sahai, Raghvendra, Stroh, Michael C., Quiroga-Nuñez, Luis Henry, van Langevelde, Huib Jan, Claussen, Mark J, Weller, Rachel
We present a method to estimate distances to Asymptotic Giant Branch (AGB) stars in the Galaxy, using spectral energy distributions (SEDs) in the near- and mid-infrared. By assuming that a given set of source properties (initial mass, stellar tempera
Externí odkaz:
http://arxiv.org/abs/2405.02459
Compared to "black-box" models, like random forests and deep neural networks, explainable boosting machines (EBMs) are considered "glass-box" models that can be competitively accurate while also maintaining a higher degree of transparency and explain
Externí odkaz:
http://arxiv.org/abs/2311.07452
Autor:
Shoghi, Nima, Kolluru, Adeesh, Kitchin, John R., Ulissi, Zachary W., Zitnick, C. Lawrence, Wood, Brandon M.
Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across
Externí odkaz:
http://arxiv.org/abs/2310.16802