Zobrazeno 1 - 10
of 68
pro vyhledávání: '"Magnússon, Sindri"'
Reinforcement learning has recently gained unprecedented popularity, yet it still grapples with sample inefficiency. Addressing this challenge, federated reinforcement learning (FedRL) has emerged, wherein agents collaboratively learn a single policy
Externí odkaz:
http://arxiv.org/abs/2404.10635
Data similarity assumptions have traditionally been relied upon to understand the convergence behaviors of federated learning methods. Unfortunately, this approach often demands fine-tuning step sizes based on the level of data similarity. When data
Externí odkaz:
http://arxiv.org/abs/2403.02347
Parallel stochastic gradient methods are gaining prominence in solving large-scale machine learning problems that involve data distributed across multiple nodes. However, obtaining unbiased stochastic gradients, which have been the focus of most theo
Externí odkaz:
http://arxiv.org/abs/2403.00853
SCANIA Component X Dataset: A Real-World Multivariate Time Series Dataset for Predictive Maintenance
This paper presents a description of a real-world, multivariate time series dataset collected from an anonymized engine component (called Component X) of a fleet of trucks from SCANIA, Sweden. This dataset includes diverse variables capturing detaile
Externí odkaz:
http://arxiv.org/abs/2401.15199
Autor:
Beikmohammadi, Ali, Hamian, Mohammad Hosein, Khoeyniha, Neda, Lindgren, Tony, Steinert, Olof, Magnússon, Sindri
The rapid influx of data-driven models into the industrial sector has been facilitated by the proliferation of sensor technology, enabling the collection of vast quantities of data. However, leveraging these models for failure detection and prognosis
Externí odkaz:
http://arxiv.org/abs/2402.08611
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or use fixed step-sizes that depend on and decrease with an upper bound of the delays. Not only are such delay bounds ha
Externí odkaz:
http://arxiv.org/abs/2312.06508
We propose a delay-agnostic asynchronous coordinate update algorithm (DEGAS) for computing operator fixed points, with applications to asynchronous optimization. DEGAS includes novel asynchronous variants of ADMM and block-coordinate descent as speci
Externí odkaz:
http://arxiv.org/abs/2305.08535
Existing asynchronous distributed optimization algorithms often use diminishing step-sizes that cause slow practical convergence, or fixed step-sizes that depend on an assumed upper bound of delays. Not only is such a delay bound hard to obtain in ad
Externí odkaz:
http://arxiv.org/abs/2303.18034
Autor:
Beikmohammadi, Ali, Magnússon, Sindri
In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative t
Externí odkaz:
http://arxiv.org/abs/2304.03291
Autor:
Beikmohammadi, Ali, Magnússon, Sindri
Reinforcement learning (RL) is crucial for data science decision-making but suffers from sample inefficiency, particularly in real-world scenarios with costly physical interactions. This paper introduces a novel human-inspired framework to enhance RL
Externí odkaz:
http://arxiv.org/abs/2303.08115