Zobrazeno 1 - 10
of 14 820
pro vyhledávání: '"A. Ekin"'
Autor:
Chen, Dong, Dethise, Alice, Akkus, Istemi Ekin, Rimac, Ivica, Satzke, Klaus, Koskela, Antti, Canini, Marco, Wang, Wei, Chen, Ruichuan
A collaboration between dataset owners and model owners is needed to facilitate effective machine learning (ML) training. During this collaboration, however, dataset owners and model owners want to protect the confidentiality of their respective asse
Externí odkaz:
http://arxiv.org/abs/2412.08534
Today, GPS-equipped mobile devices are ubiquitous, and they generate Location-Based Service (LBS) data, which has become a critical resource for understanding human mobility. However, inherent limitations in LBS datasets, primarily characterized by d
Externí odkaz:
http://arxiv.org/abs/2411.16595
Language models have shown impressive performance on tasks within their training distribution, but often struggle with novel problems requiring complex reasoning. We investigate the effectiveness of test-time training (TTT) -- updating model paramete
Externí odkaz:
http://arxiv.org/abs/2411.07279
This work examines the fairness of generative mobility models, addressing the often overlooked dimension of equity in model performance across geographic regions. Predictive models built on crowd flow data are instrumental in understanding urban stru
Externí odkaz:
http://arxiv.org/abs/2411.04453
Simulation-Based Optimistic Policy Iteration For Multi-Agent MDPs with Kullback-Leibler Control Cost
This paper proposes an agent-based optimistic policy iteration (OPI) scheme for learning stationary optimal stochastic policies in multi-agent Markov Decision Processes (MDPs), in which agents incur a Kullback-Leibler (KL) divergence cost for their c
Externí odkaz:
http://arxiv.org/abs/2410.15156
Autor:
Yau, Morris, Akyürek, Ekin, Mao, Jiayuan, Tenenbaum, Joshua B., Jegelka, Stefanie, Andreas, Jacob
Previous research has explored the computational expressivity of Transformer models in simulating Boolean circuits or Turing machines. However, the learnability of these simulators from observational data has remained an open question. Our study addr
Externí odkaz:
http://arxiv.org/abs/2410.10101
Autor:
Gangan, Abhijeet S., Schoenholz, Samuel S., Cubuk, Ekin Dogus, Bauchy, Mathieu, Krishnan, N. M. Anoop
The accuracy of atomistic simulations depends on the precision of force fields. Traditional numerical methods often struggle to optimize the empirical force field parameters for reproducing target properties. Recent approaches rely on training these
Externí odkaz:
http://arxiv.org/abs/2409.13844
Autor:
Yang, Sherry, Batzner, Simon, Gao, Ruiqi, Aykol, Muratahan, Gaunt, Alexander L., McMorrow, Brendan, Rezende, Danilo J., Schuurmans, Dale, Mordatch, Igor, Cubuk, Ekin D.
Generative models trained at scale can now produce text, video, and more recently, scientific data such as crystal structures. In applications of generative approaches to materials science, and in particular to crystal structures, the guidance from t
Externí odkaz:
http://arxiv.org/abs/2409.06762
Autor:
Baytaş, Bekir, Derin, Ozan Ekin
This brief brochure is intended to present a philosophical theory known as relational materialism. We introduce the postulates and principles of the theory, articulating its ontological and epistemological content using the language of category theor
Externí odkaz:
http://arxiv.org/abs/2409.02487
Autor:
Taskin, Ekin, Haro, Juan Luis Villarreal, Girard, Gabriel, Rafael-Patiño, Jonathan, Garyfallidis, Eleftherios, Thiran, Jean-Philippe, Canales-Rodríguez, Erick Jorge
Constrained Spherical Deconvolution (CSD) is crucial for estimating white matter fiber orientations using diffusion MRI data. A relevant parameter in CSD is the maximum order $l_{max}$ used in the spherical harmonics series, influencing the angular r
Externí odkaz:
http://arxiv.org/abs/2408.12921