Zobrazeno 1 - 10
of 22
pro vyhledávání: '"Beikmohammadi, Ali"'
Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this field b
Externí odkaz:
http://arxiv.org/abs/2406.01455
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
Distributed 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 t
Externí odkaz:
http://arxiv.org/abs/2403.00853
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
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
Autor:
Beikmohammadi, Ali
Among many clustering algorithms, the K-means clustering algorithm is widely used because of its simple algorithm and fast convergence. However, this algorithm suffers from incomplete data, where some samples have missed some of their attributes. To
Externí odkaz:
http://arxiv.org/abs/2212.12379
Autor:
Beikmohammadi, Ali
One of the realistic scenarios is taking a sequence of optimal actions to do a task. Reinforcement learning is the most well-known approach to deal with this kind of task in the machine learning community. Finding a suitable alternative could always
Externí odkaz:
http://arxiv.org/abs/2212.12517
Autor:
Beikmohammadi, Ali, Magnússon, Sindri
Publikováno v:
In Information Sciences March 2024 661