Zobrazeno 1 - 10
of 15 257
pro vyhledávání: '"Duffield AT"'
Autor:
Aifer, Maxwell, Duffield, Samuel, Donatella, Kaelan, Melanson, Denis, Klett, Phoebe, Belateche, Zach, Crooks, Gavin, Martinez, Antonio J., Coles, Patrick J.
A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including model selection. However, the intractability of sampling Ba
Externí odkaz:
http://arxiv.org/abs/2410.01793
This study introduces a novel approach to robot-assisted ankle rehabilitation by proposing a Dual-Agent Multiple Model Reinforcement Learning (DAMMRL) framework, leveraging multiple model adaptive control (MMAC) and co-adaptive control strategies. In
Externí odkaz:
http://arxiv.org/abs/2407.21734
Granger causality, commonly used for inferring causal structures from time series data, has been adopted in widespread applications across various fields due to its intuitive explainability and high compatibility with emerging deep neural network pre
Externí odkaz:
http://arxiv.org/abs/2406.10419
Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior distribution. In this work, we (i) introduce posteriors, an easily ex
Externí odkaz:
http://arxiv.org/abs/2406.00104
Autor:
Donatella, Kaelan, Duffield, Samuel, Aifer, Maxwell, Melanson, Denis, Crooks, Gavin, Coles, Patrick J.
Second-order training methods have better convergence properties than gradient descent but are rarely used in practice for large-scale training due to their computational overhead. This can be viewed as a hardware limitation (imposed by digital compu
Externí odkaz:
http://arxiv.org/abs/2405.13817
In the transformative landscape of smart cities, the integration of the cutting-edge web technologies into time series forecasting presents a pivotal opportunity to enhance urban planning, sustainability, and economic growth. The advancement of deep
Externí odkaz:
http://arxiv.org/abs/2405.05430
Autor:
Mohseni, Peiman, Duffield, Nick
Conditional Neural Processes (CNPs) constitute a family of probabilistic models that harness the flexibility of neural networks to parameterize stochastic processes. Their capability to furnish well-calibrated predictions, combined with simple maximu
Externí odkaz:
http://arxiv.org/abs/2404.13182
Autor:
Cabezas, Alberto, Corenflos, Adrien, Lao, Junpeng, Louf, Rémi, Carnec, Antoine, Chaudhari, Kaustubh, Cohn-Gordon, Reuben, Coullon, Jeremie, Deng, Wei, Duffield, Sam, Durán-Martín, Gerardo, Elantkowski, Marcin, Foreman-Mackey, Dan, Gregori, Michele, Iguaran, Carlos, Kumar, Ravin, Lysy, Martin, Murphy, Kevin, Orduz, Juan Camilo, Patel, Karm, Wang, Xi, Zinkov, Rob
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorithms' implementation. BlackJAX
Externí odkaz:
http://arxiv.org/abs/2402.10797
We show the existence of families of periodic polyhedra in spaces of constant curvature whose fundamental domains can be obtained by attaching prisms and antiprisms to Archimedean solids. These polyhedra have constant discrete curvature and are weakl
Externí odkaz:
http://arxiv.org/abs/2401.04031
Thermodynamic computing exploits fluctuations and dissipation in physical systems to efficiently solve various mathematical problems. For example, it was recently shown that certain linear algebra problems can be solved thermodynamically, leading to
Externí odkaz:
http://arxiv.org/abs/2311.12759