Zobrazeno 1 - 10
of 992
pro vyhledávání: '"Yildirim,Murat"'
Advancements in sensor technology offer significant insights into vehicle conditions, unlocking new venues to enhance fleet operations. While current vehicle health management models provide accurate predictions of vehicle failures, they often fail t
Externí odkaz:
http://arxiv.org/abs/2412.04350
Operations and maintenance (O&M) is a fundamental problem in wind energy systems with far reaching implications for reliability and profitability. Optimizing O&M is a multi-faceted decision optimization problem that requires a careful balancing act a
Externí odkaz:
http://arxiv.org/abs/2410.24052
Continual Learning (CL) methods usually learn from all available data. However, this is not the case in human cognition which efficiently focuses on key experiences while disregarding the redundant information. Similarly, not all data points in a dat
Externí odkaz:
http://arxiv.org/abs/2410.17715
Autor:
Chowdhury, Md Tawsif Rahman, Vo, Huynh Quang Nguyen, Ramanan, Paritosh, Yildirim, Murat, Tutuncuoglu, Gozde
The Von Neumann bottleneck, a fundamental challenge in conventional computer architecture, arises from the inability to execute fetch and data operations simultaneously due to a shared bus linking processing and memory units. This bottleneck signific
Externí odkaz:
http://arxiv.org/abs/2409.06140
Autor:
Singh, Prabhant, Gijsbers, Pieter, Yildirim, Murat Onur, Gok, Elif Ceren, Vanschoren, Joaquin
We propose an AutoML system that enables model selection on clustering problems by leveraging optimal transport-based dataset similarity. Our objective is to establish a comprehensive AutoML pipeline for clustering problems and provide recommendation
Externí odkaz:
http://arxiv.org/abs/2407.11286
In distributed machine learning, efficient training across multiple agents with different data distributions poses significant challenges. Even with a centralized coordinator, current algorithms that achieve optimal communication complexity typically
Externí odkaz:
http://arxiv.org/abs/2407.02689
Autor:
Yildirim, Murat Onur, Yildirim, Elif Ceren Gok, Mocanu, Decebal Constantin, Vanschoren, Joaquin
Class incremental learning (CIL) in an online continual learning setting strives to acquire knowledge on a series of novel classes from a data stream, using each data point only once for training. This is more realistic compared to offline modes, whe
Externí odkaz:
http://arxiv.org/abs/2403.14684
Effective operations and maintenance (O&M) in modern production systems hinges on careful orchestration of economic and degradation dependencies across a multitude of assets. While the economic dependencies are well studied, degradation dependencies
Externí odkaz:
http://arxiv.org/abs/2311.10966
Autor:
Altinpulluk, Nur Banu, Altinpulluk, Deniz, Ramanan, Paritosh, Paulson, Noah, Qiu, Feng, Babinec, Susan, Yildirim, Murat
Battery diagnosis, prognosis and health management models play a critical role in the integration of battery systems in energy and mobility fields. However, large-scale deployment of these models is hindered by a myriad of challenges centered around
Externí odkaz:
http://arxiv.org/abs/2310.09628
Autor:
Yildirim, Murat Onur, Yildirim, Elif Ceren Gok, Sokar, Ghada, Mocanu, Decebal Constantin, Vanschoren, Joaquin
Continual learning (CL) refers to the ability of an intelligent system to sequentially acquire and retain knowledge from a stream of data with as little computational overhead as possible. To this end; regularization, replay, architecture, and parame
Externí odkaz:
http://arxiv.org/abs/2308.14831