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pro vyhledávání: '"Pan, Rong"'
An early warning of future system failure is essential for conducting predictive maintenance and enhancing system availability. This paper introduces a three-step framework for assessing system health to predict imminent system breakdowns. First, the
Externí odkaz:
http://arxiv.org/abs/2410.22594
Autor:
Elrefaey, Abdelmonem, Pan, Rong
This paper presents a novel Integer Programming (IP) approach for discovering the Markov Equivalent Class (MEC) of Bayesian Networks (BNs) through observational data. The MEC-IP algorithm utilizes a unique clique-focusing strategy and Extended Maxima
Externí odkaz:
http://arxiv.org/abs/2410.18147
To enhance the generalization of machine learning models to unseen data, techniques such as dropout, weight decay ($L_2$ regularization), and noise augmentation are commonly employed. While regularization methods (i.e., dropout and weight decay) are
Externí odkaz:
http://arxiv.org/abs/2410.14602
As machine learning models continue to swiftly advance, calibrating their performance has become a major concern prior to practical and widespread implementation. Most existing calibration methods often negatively impact model accuracy due to the lac
Externí odkaz:
http://arxiv.org/abs/2410.10864
Autor:
Le, Yanfang, Pan, Rong, Newman, Peter, Blendin, Jeremias, Kabbani, Abdul, Jain, Vipin, Sivaramu, Raghava, Matus, Francis
Emerging artificial intelligence (AI) and machine learning (ML) workloads present new challenges of managing the collective communication used in distributed training across hundreds or even thousands of GPUs. This paper presents STrack, a novel hard
Externí odkaz:
http://arxiv.org/abs/2407.15266
As crowdsourcing emerges as an efficient and cost-effective method for obtaining labels for machine learning datasets, it is important to assess the quality of crowd-provided data, so as to improve analysis performance and reduce biases in subsequent
Externí odkaz:
http://arxiv.org/abs/2404.17582
Autor:
Bonato, Tommaso, Kabbani, Abdul, De Sensi, Daniele, Pan, Rong, Le, Yanfang, Raiciu, Costin, Handley, Mark, Schneider, Timo, Blach, Nils, Ghalayini, Ahmad, Alves, Daniel, Papamichael, Michael, Caulfield, Adrian, Hoefler, Torsten
The increasing demand of machine learning (ML) workloads in datacenters places significant stress on current congestion control (CC) algorithms, many of which struggle to maintain performance at scale. These workloads generate bursty, synchronized tr
Externí odkaz:
http://arxiv.org/abs/2404.01630
Plan synthesis aims to generate a course of actions or policies to transit given initial states to goal states, provided domain models that could be designed by experts or learnt from training data or interactions with the world. Intrigued by the cla
Externí odkaz:
http://arxiv.org/abs/2403.00783
Change-point detection (CPD) is crucial for identifying abrupt shifts in data, which influence decision-making and efficient resource allocation across various domains. To address the challenges posed by the costly and time-intensive data acquisition
Externí odkaz:
http://arxiv.org/abs/2312.03176