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Related papers: Approximating G(t)/GI/1 queues with deep learning

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Training deep recurrent neural network (RNN) architectures is complicated due to the increased network complexity. This disrupts the learning of higher order abstracts using deep RNN. In case of feed-forward networks training deep…

Computation and Language · Computer Science 2018-08-07 Murali Karthick Baskar , Martin Karafiat , Lukas Burget , Karel Vesely , Frantisek Grezl , Jan Honza Cernocky

Time-frequency analysis is an important and challenging task in many applications. Fourier and wavelet analysis are two classic methods that have achieved remarkable success in many fields. However, they also exhibit limitations when…

Machine Learning · Computer Science 2024-10-25 Feng Zhou , Antonio Cicone , Haomin Zhou

In this work, nonparametric statistical inference is provided for the continuous-time M/G/1 queueing model from a Bayesian point of view. The inference is based on observations of the inter-arrival and service times. Beside other…

Statistics Theory · Mathematics 2017-03-22 Cornelia Wichelhaus , Moritz von Rohrscheidt

Active researches are currently being performed to incorporate the wealth of scientific knowledge into data-driven approaches (e.g., neural networks) in order to improve the latter's effectiveness. In this study, the Theory-guided Neural…

Machine Learning · Computer Science 2020-03-03 Nanzhe Wang , Dongxiao Zhang , Haibin Chang , Heng Li

Traffic forecasting is vital for Intelligent Transportation Systems, for which Machine Learning (ML) methods have been extensively explored to develop data-driven Artificial Intelligence (AI) solutions. Recent research focuses on modelling…

Machine Learning · Computer Science 2025-05-01 Xiao Zheng , Saeed Asadi Bagloee , Majid Sarvi

In several natural language tasks, labeled sequences are available in separate domains (say, languages), but the goal is to label sequences with mixed domain (such as code-switched text). Or, we may have available models for labeling whole…

Machine Learning · Computer Science 2018-12-27 Divam Gupta , Tanmoy Chakraborty , Soumen Chakrabarti

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these…

Machine Learning · Computer Science 2018-04-20 Guokun Lai , Wei-Cheng Chang , Yiming Yang , Hanxiao Liu

Numerical simulators are essential tools in the study of natural fluid-systems, but their performance often limits application in practice. Recent machine-learning approaches have demonstrated their ability to accelerate spatio-temporal…

Fluid Dynamics · Physics 2022-05-06 Mario Lino , Stathi Fotiadis , Anil A. Bharath , Chris Cantwell

We study a single server FIFO queue that offers general service. Each of n customers enter the queue at random time epochs that are inde- pendent and identically distributed. We call this the random scattering traffic model, and the…

Probability · Mathematics 2017-08-21 Peter W. Glynn , Harsha Honnappa

In this paper, we study the $G/\mathit{GI}/N$ queue in the Halfin--Whitt regime. Our first result is to obtain a deterministic fluid limit for the properly centered and scaled number of customers in the system which may be used to provide a…

Probability · Mathematics 2009-12-16 Josh Reed

We explore the interplay of quantum computing and machine learning to advance experimental protocols for observing measurement-induced phase transitions (MIPT) in quantum devices. In particular, we focus on trapped ion monitored circuits…

Quantum Physics · Physics 2025-11-07 Yangrui Hu , Yi Hong Teoh , William Witczak-Krempa , Roger G. Melko

Deep learning has achieved impressive prediction performance in the field of sequence learning recently. Dissolved oxygen prediction, as a kind of time-series forecasting, is suitable for this technique. Although many researchers have…

Signal Processing · Electrical Eng. & Systems 2019-11-22 Hongqian Qin

Over the past decade, deep learning research has been accelerated by increasingly powerful hardware, which facilitated rapid growth in the model complexity and the amount of data ingested. This is becoming unsustainable and therefore…

Machine Learning · Computer Science 2024-02-08 Damian Owerko , Charilaos I. Kanatsoulis , Alejandro Ribeiro

Edge intelligence has arisen as a promising computing paradigm for supporting miscellaneous smart applications that rely on machine learning techniques. While the community has extensively investigated multi-tier edge deployment for…

Distributed, Parallel, and Cluster Computing · Computer Science 2022-11-01 Liekang Zeng , Chongyu Yang , Peng Huang , Zhi Zhou , Shuai Yu , Xu Chen

Recurrent Neural Networks (RNNs) continue to show outstanding performance in sequence modeling tasks. However, training RNNs on long sequences often face challenges like slow inference, vanishing gradients and difficulty in capturing long…

Artificial Intelligence · Computer Science 2018-02-06 Victor Campos , Brendan Jou , Xavier Giro-i-Nieto , Jordi Torres , Shih-Fu Chang

Graph processes model a number of important problems such as identifying the epicenter of an earthquake or predicting weather. In this paper, we propose a Graph Convolutional Recurrent Neural Network (GCRNN) architecture specifically…

Machine Learning · Computer Science 2019-06-28 Luana Ruiz , Fernando Gama , Alejandro Ribeiro

Traffic prediction is the cornerstone of an intelligent transportation system. Accurate traffic forecasting is essential for the applications of smart cities, i.e., intelligent traffic management and urban planning. Although various methods…

Machine Learning · Computer Science 2021-05-04 Fuxian Li , Jie Feng , Huan Yan , Guangyin Jin , Depeng Jin , Yong Li

Time series forecasting is an extensively studied subject in statistics, economics, and computer science. Exploration of the correlation and causation among the variables in a multivariate time series shows promise in enhancing the…

Machine Learning · Computer Science 2021-04-22 Chao Shang , Jie Chen , Jinbo Bi

Diffusion generative models have emerged as a powerful framework for addressing problems in structural biology and structure-based drug design. These models operate directly on 3D molecular structures. Due to the unfavorable scaling of…

Biomolecules · Quantitative Biology 2024-05-10 Ian Dunn , David Ryan Koes

Data-driven approaches to automated machine condition monitoring are gaining popularity due to advancements made in sensing technologies and computing algorithms. This paper proposes the use of a deep learning model, based on Long…

Signal Processing · Electrical Eng. & Systems 2019-07-30 Jianlei Zhang , Binil Starly