Zobrazeno 1 - 10
of 14 560
pro vyhledávání: '"Ekin, A."'
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
In this work, we develop a differentiable rendering pipeline for visualising plasma emission within tokamaks, and estimating the gradients of the emission and estimating other physical quantities. Unlike prior work, we are able to leverage arbitrary
Externí odkaz:
http://arxiv.org/abs/2408.07555
Autor:
Li, Zheng, Wu, Siyuan, Chen, Ruichuan, Aditya, Paarijaat, Akkus, Istemi Ekin, Vanga, Manohar, Zhang, Min, Li, Hao, Zhang, Yang
Machine learning (ML), driven by prominent paradigms such as centralized and federated learning, has made significant progress in various critical applications ranging from autonomous driving to face recognition. However, its remarkable success has b
Externí odkaz:
http://arxiv.org/abs/2408.02131