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Related papers: Strong Embeddings for Transitory Queueing Models

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A generalisation of Takens' delay-coordinate embedding theorem to stochastic systems, the Stochastic Embedding Sufficiency Theorem, is an inverse methodology enabling non-parametric recovery of both drift and diffusion fields from scalar…

Statistical Mechanics · Physics 2026-05-12 Carolina Garcia , Lucía Perea Durán , Agnese Venezia , Alex Conradie

A wide class of non-Markovian completely positive master equations can be formulated on the basis of quantum collisional models. In this phenomenological approach the dynamics of an open quantum system is modeled through an ensemble of…

Quantum Physics · Physics 2013-09-26 Adrian A. Budini

We consider a stationary Markov process that models certain queues with a bulk service of a fixed number $m$ of admitted customers. We find an integral expression of its transition probability function in terms of certain multi-orthogonal…

Probability · Mathematics 2023-08-29 Ulises Fidalgo

Time series forecasting is essential for many practical applications, with the adoption of transformer-based models on the rise due to their impressive performance in NLP and CV. Transformers' key feature, the attention mechanism,…

Machine Learning · Computer Science 2024-02-09 PeiSong Niu , Tian Zhou , Xue Wang , Liang Sun , Rong Jin

The widespread use of GPS devices has driven advances in spatiotemporal data mining, enabling machine learning models to simulate human decision making and generate realistic trajectories, addressing both data collection costs and privacy…

Machine Learning · Computer Science 2025-10-09 Zhiyang Zhang , Ningcong Chen , Xin Zhang , Yanhua Li , Shen Su , Hui Lu , Jun Luo

Data-driven approaches such as deep learning can result in predictive models for material properties with exceptional accuracy and efficiency. However, in many applications, data is sparse, severely limiting their accuracy and…

Machine Learning · Computer Science 2025-10-29 Robert J Appleton , Brian C Barnes , Alejandro Strachan

Large language models (LLMs) have revolutionized natural language processing by achieving state-of-the-art performance across various tasks. Recently, their effectiveness as embedding models has gained attention, marking a paradigm shift…

Computation and Language · Computer Science 2025-07-28 Chongyang Tao , Tao Shen , Shen Gao , Junshuo Zhang , Zhen Li , Kai Hua , Wenpeng Hu , Zhengwei Tao , Shuai Ma

Tying the weights of the target word embeddings with the target word classifiers of neural machine translation models leads to faster training and often to better translation quality. Given the success of this parameter sharing, we…

Computation and Language · Computer Science 2018-09-03 Nikolaos Pappas , Lesly Miculicich Werlen , James Henderson

Superdiffusion is an anomalous transport behavior. Recently, a new mechanism, termed the ``nodal mechanism," has been proposed to induce superdiffusion in quantum models. However, existing realizations of the nodal mechanism have so far…

Mesoscale and Nanoscale Physics · Physics 2025-11-14 Shaofeng Huang , Yu-Peng Wang , Jie Ren , Chen Fang

Constitutive and closure models play important roles in computational mechanics and computational physics in general. Classical constitutive models for solid and fluid materials are typically local, algebraic equations or flow rules…

Fluid Dynamics · Physics 2021-06-16 Xu-Hui Zhou , Jiequn Han , Heng Xiao

Results in interpretability suggest that large vision and language models learn implicit linear encodings when models are biased by in-context prompting. However, the existence of similar linear representations in more general adaptation…

Machine Learning · Computer Science 2025-12-18 Darrin O' Brien , Dhikshith Gajulapalli , Eric Xia

Generative models aim to learn the probability distributions underlying data, enabling the generation of new, realistic samples. Quantum inspired generative models, such as Born machines based on the matrix product state framework, have…

Machine Learning · Computer Science 2025-12-30 Wanda Hou , Miao Li , Yi-Zhuang You

Cognitive processes undergo various fluctuations and transient states across different temporal scales. Superstatistics are emerging as a flexible framework for incorporating such non-stationary dynamics into existing cognitive model…

Neurons and Cognition · Quantitative Biology 2024-10-02 Lukas Schumacher , Martin Schnuerch , Andreas Voss , Stefan T. Radev

We provide finite-sample distribution approximations, that are uniform in the parameter, for inference in linear mixed models. Focus is on variances and covariances of random effects in cases where existing theory fails because their…

Statistics Theory · Mathematics 2025-07-29 Karl Oskar Ekvall , Matteo Bottai

Nonlinear time series models with exogenous regressors are essential in econometrics, queuing theory, and machine learning, though their statistical analysis remains incomplete. Key results, such as the law of large numbers and the…

Statistics Theory · Mathematics 2025-10-24 Attila Lovas

Token embeddings play a crucial role in language modeling but, despite this practical relevance, their theoretical understanding remains limited. Our paper addresses the gap by characterizing the structure of embeddings obtained via…

Machine Learning · Computer Science 2025-06-26 Diyuan Wu , Aleksandr Shevchenko , Samet Oymak , Marco Mondelli

Closed queuing networks with finite capacity buffers and skip-over policies are fundamental models in the performance evaluation of computer and communication systems. This technical report presents the details of computational algorithms…

Performance · Computer Science 2024-09-13 Gianfranco Balbo , Andrea Marin , Diletta Olliaro , Matteo Sereno

Large-eddy simulation developments and validations are presented for an improved simulation of turbulent internal flows. Numerical methods are proposed according to two competing criteria: numerical qualities (precision and spectral…

Fluid Dynamics · Physics 2008-01-15 Jérôme Boudet , Joëlle Caro , L. Shao , Emmanuel Lévêque

Mean embeddings provide an extremely flexible and powerful tool in machine learning and statistics to represent probability distributions and define a semi-metric (MMD, maximum mean discrepancy; also called N-distance or energy distance),…

Machine Learning · Statistics 2019-05-17 Matthieu Lerasle , Zoltan Szabo , Timothee Mathieu , Guillaume Lecue

The theory of low-rank tensor-train approximation is well understood when the approximation error is measured in the Frobenius norm. The entrywise maximum norm is equally important but is significantly weaker for large tensors, making the…

Numerical Analysis · Mathematics 2025-04-09 Stanislav Budzinskiy
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