Zobrazeno 1 - 10
of 14 970
pro vyhledávání: '"Whiteson"'
Publikováno v:
PLOS Global Public Health, Vol 4, Iss 5, p e0002738 (2024)
The novel Coronavirus Disease 19 (COVID-19) caused devastating effects globally, and healthcare workers were among the most affected by the pandemic. Despite healthcare workers being prioritized in COVID-19 vaccination globally and in Ghana, hesitanc
Externí odkaz:
https://doaj.org/article/0b7b3b234f8f499eaa1974b976c5a0f3
Autor:
Sha, Qiyu, Murnane, Daniel, Fieg, Max, Tong, Shelley, Zakharyan, Mark, Fang, Yaquan, Whiteson, Daniel
Analysis of data from particle physics experiments traditionally sacrifices some sensitivity to new particles for the sake of practical computability, effectively ignoring some potentially striking signatures. However, recent advances in ML-based tra
Externí odkaz:
http://arxiv.org/abs/2410.00269
Autor:
Goldie, Alexander David, Lu, Chris, Jackson, Matthew Thomas, Whiteson, Shimon, Foerster, Jakob Nicolaus
While reinforcement learning (RL) holds great potential for decision making in the real world, it suffers from a number of unique difficulties which often need specific consideration. In particular: it is highly non-stationary; suffers from high degr
Externí odkaz:
http://arxiv.org/abs/2407.07082
In many real-world settings, an agent must learn to act in environments where no reward signal can be specified, but a set of expert demonstrations is available. Imitation learning (IL) is a popular framework for learning policies from such demonstra
Externí odkaz:
http://arxiv.org/abs/2407.00495
Autor:
Mahjourian, Reza, Mu, Rongbing, Likhosherstov, Valerii, Mougin, Paul, Huang, Xiukun, Messias, Joao, Whiteson, Shimon
This paper introduces UniGen, a novel approach to generating new traffic scenarios for evaluating and improving autonomous driving software through simulation. Our approach models all driving scenario elements in a unified model: the position of new
Externí odkaz:
http://arxiv.org/abs/2405.03807
Autor:
Huetsch, Nathan, Villadamigo, Javier Mariño, Shmakov, Alexander, Diefenbacher, Sascha, Mikuni, Vinicius, Heimel, Theo, Fenton, Michael, Greif, Kevin, Nachman, Benjamin, Whiteson, Daniel, Butter, Anja, Plehn, Tilman
Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches ar
Externí odkaz:
http://arxiv.org/abs/2404.18807
Autor:
Shmakov, Alexander, Greif, Kevin, Fenton, Michael James, Ghosh, Aishik, Baldi, Pierre, Whiteson, Daniel
The measurements performed by particle physics experiments must account for the imperfect response of the detectors used to observe the interactions. One approach, unfolding, statistically adjusts the experimental data for detector effects. Recently,
Externí odkaz:
http://arxiv.org/abs/2404.14332
Autor:
Jackson, Matthew Thomas, Matthews, Michael Tryfan, Lu, Cong, Ellis, Benjamin, Whiteson, Shimon, Foerster, Jakob
In many real-world settings, agents must learn from an offline dataset gathered by some prior behavior policy. Such a setting naturally leads to distribution shift between the behavior policy and the target policy being trained - requiring policy con
Externí odkaz:
http://arxiv.org/abs/2404.06356
A core ambition of reinforcement learning (RL) is the creation of agents capable of rapid learning in novel tasks. Meta-RL aims to achieve this by directly learning such agents. Black box methods do so by training off-the-shelf sequence models end-to
Externí odkaz:
http://arxiv.org/abs/2403.03020
Autor:
Brandes, Len, Modi, Chirag, Ghosh, Aishik, Farrell, Delaney, Lindblom, Lee, Heinrich, Lukas, Steiner, Andrew W., Weber, Fridolin, Whiteson, Daniel
Neutron stars provide a unique opportunity to study strongly interacting matter under extreme density conditions. The intricacies of matter inside neutron stars and their equation of state are not directly visible, but determine bulk properties, such
Externí odkaz:
http://arxiv.org/abs/2403.00287