Zobrazeno 1 - 10
of 7 019
pro vyhledávání: '"A Brehmer"'
Autor:
Brehmer, Johann, Bresó, Víctor, de Haan, Pim, Plehn, Tilman, Qu, Huilin, Spinner, Jonas, Thaler, Jesse
We show that the Lorentz-Equivariant Geometric Algebra Transformer (L-GATr) yields state-of-the-art performance for a wide range of machine learning tasks at the Large Hadron Collider. L-GATr represents data in a geometric algebra over space-time and
Externí odkaz:
http://arxiv.org/abs/2411.00446
Given large data sets and sufficient compute, is it beneficial to design neural architectures for the structure and symmetries of each problem? Or is it more efficient to learn them from data? We study empirically how equivariant and non-equivariant
Externí odkaz:
http://arxiv.org/abs/2410.23179
Communicating data insights in an accessible and engaging manner to a broader audience remains a significant challenge. To address this problem, we introduce the Emoji Encoder, a tool that generates a set of emoji recommendations for the field and ca
Externí odkaz:
http://arxiv.org/abs/2408.13418
To enable data-driven decision-making across organizations, data professionals need to share insights with their colleagues in context-appropriate communication channels. Many of their colleagues rely on data but are not themselves analysts; furtherm
Externí odkaz:
http://arxiv.org/abs/2408.00242
Augmented video presentation tools provide a natural way for presenters to interact with their content, resulting in engaging experiences for remote audiences, such as when a presenter uses hand gestures to manipulate and direct attention to visual a
Externí odkaz:
http://arxiv.org/abs/2406.17986
Modelling the propagation of electromagnetic wireless signals is critical for designing modern communication systems. Wireless ray tracing simulators model signal propagation based on the 3D geometry and other scene parameters, but their accuracy is
Externí odkaz:
http://arxiv.org/abs/2406.14995
The temporal dynamics of quantitative metrics or key performance indicators (KPIs) are central to decision-making in enterprise organizations. Recently, major business intelligence providers have introduced new infrastructure for defining, sharing, a
Externí odkaz:
http://arxiv.org/abs/2406.03415
Extracting scientific understanding from particle-physics experiments requires solving diverse learning problems with high precision and good data efficiency. We propose the Lorentz Geometric Algebra Transformer (L-GATr), a new multi-purpose architec
Externí odkaz:
http://arxiv.org/abs/2405.14806
Testing earthquake forecasts is essential to obtain scientific information on forecasting models and sufficient credibility for societal usage. We aim at enhancing the testing phase proposed by the Collaboratory for the Study of Earthquake Predictabi
Externí odkaz:
http://arxiv.org/abs/2405.10712
We explore the viability of casting foundation models as generic reward functions for reinforcement learning. To this end, we propose a simple pipeline that interfaces an off-the-shelf vision model with a large language model. Specifically, given a t
Externí odkaz:
http://arxiv.org/abs/2312.03881