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Related papers: Amplitude Surrogates for Multi-Jet Processes

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Tensor decomposition of convolutional and fully-connected layers is an effective way to reduce parameters and FLOP in neural networks. Due to memory and power consumption limitations of mobile or embedded devices, the quantization step is…

Machine Learning · Computer Science 2023-08-10 Daria Cherniuk , Stanislav Abukhovich , Anh-Huy Phan , Ivan Oseledets , Andrzej Cichocki , Julia Gusak

Resource-efficient and high-precision approximate synthesis of quantum circuits expressed in the Clifford+T gate set is vital for Fault-Tolerant quantum computing. Efficient optimal methods are known for single-qubit RZ unitaries, otherwise…

Quantum Physics · Physics 2026-04-27 Mathias Weiden , Justin Kalloor , John Kubiatowicz , Ed Younis , Costin Iancu

We propose a general algorithmic framework for constrained matrix and tensor factorization, which is widely used in signal processing and machine learning. The new framework is a hybrid between alternating optimization (AO) and the…

Machine Learning · Statistics 2016-08-24 Kejun Huang , Nicholas D. Sidiropoulos , Athanasios P. Liavas

Multivariate techniques based on engineered features have found wide adoption in the identification of jets resulting from hadronic top decays at the Large Hadron Collider (LHC). Recent Deep Learning developments in this area include the…

High Energy Physics - Experiment · Physics 2017-11-27 Shannon Egan , Wojciech Fedorko , Alison Lister , Jannicke Pearkes , Colin Gay

As the Internet developed rapidly, it is important to choose suitable web services from a wide range of candidates. Quality of service (QoS) describes the performance of a web service dynamically with respect to the service requested by the…

Machine Learning · Computer Science 2022-12-06 Jiajia Mi , Hao Wu

This paper proposes a machine-learning-based solution approach for solving multi-horizon stochastic programs. The approach embeds a deep learning neural network into a multi-horizon stochastic program to approximate the recourse operational…

Optimization and Control · Mathematics 2025-12-03 Hongyu Zhang , Gabriele Sormani , Enza Messina , Alan King , Francesca Maggioni

The last decade has witnessed the breakthrough of deep neural networks (DNNs) in many fields. With the increasing depth of DNNs, hundreds of millions of multiply-and-accumulate (MAC) operations need to be executed. To accelerate such…

Hardware Architecture · Computer Science 2022-11-29 Amro Eldebiky , Grace Li Zhang , Georg Boecherer , Bing Li , Ulf Schlichtmann

The method of surrogates is widely used in the field of nonlinear data analysis for testing for weak nonlinearities. The two most commonly used algorithms for generating surrogates are the amplitude adjusted Fourier transform (AAFT) and the…

Data Analysis, Statistics and Probability · Physics 2016-12-21 Christoph Raeth , Roberto A. Monetti

With more wind farms clustered for integration, the short-term wind speed prediction of such wind farm clusters is critical for normal operation of power systems. This paper focuses on achieving accurate, fast, and robust wind speed…

Machine Learning · Computer Science 2026-02-05 Mumin Zhang , Haochen Zhang , Xin Zhi Khoo , Yilin Zhang , Nuo Chen , Ting Zhang , Junjie Tang

The irreducible background from Z(nunu)+jets, to beyond the Standard Model searches at the LHC, can be calibrated using gamma+jets data. The method utilises the fact that at high vector boson pT, the event kinematics are the same for the…

High Energy Physics - Phenomenology · Physics 2015-05-28 S. Ask , M. A. Parker , T. Sandoval , M. E. Shea , W. J. Stirling

In the Large Hardron Collider (LHC), multiple proton-proton collisions cause pileup in reconstructing energy information for a single primary collision (jet). This project aims to select the most important features and create a model to…

High Energy Physics - Phenomenology · Physics 2015-12-18 Vein S Kong , Jiakun Li , Yujia Zhang

For high-pT forward processes at the Large Hadron Collider (LHC), QCD logarithmic corrections in the hard transverse momentum and in the large rapidity interval may both be quantitatively significant. The theoretical framework to resum…

High Energy Physics - Phenomenology · Physics 2011-01-04 M. Deak , F. Hautmann , H. Jung , K. Kutak

Jet grooming algorithms are widely used in experimental analyses at hadron colliders to remove contaminating radiation from within jets. While the algorithms perform a great service to the experiments, their intricate algorithmic structure…

High Energy Physics - Phenomenology · Physics 2016-08-24 Christopher Frye , Andrew J. Larkoski , Matthew D. Schwartz , Kai Yan

One- and two-jet inclusive quantities in hadron collisions have already been calculated to next-to-leading order accuracy, using both the subtraction and the cone method. Since the one-loop corrections have recently been obtained for all…

High Energy Physics - Phenomenology · Physics 2009-10-28 S. Frixione , Z. Kunszt , A. Signer

Various factorization-based methods have been proposed to leverage second-order, or higher-order cross features for boosting the performance of predictive models. They generally enumerate all the cross features under a predefined maximum…

Machine Learning · Computer Science 2020-06-25 Weiyu Cheng , Yanyan Shen , Linpeng Huang

We generalise the factorization of abelian gauge theory amplitudes to next-to-leading power (NLP) in a soft scale expansion, following a recent generalisation for Yukawa theory. From an all-order power counting analysis of leading and…

High Energy Physics - Phenomenology · Physics 2021-03-01 E. Laenen , J. Sinninghe Damsté , L. Vernazza , W. Waalewijn , L. Zoppi

The evaluation of one-loop matrix elements is one of the main bottlenecks in precision calculations for the high-luminosity phase of the Large Hadron Collider. To alleviate this problem, a new C++ interface to the MCFM parton-level Monte…

High Energy Physics - Phenomenology · Physics 2022-01-05 John M Campbell , Stefan Höche , Christian T Preuss

Accurate representation to an academic network is of great significance to academic relationship mining like predicting scientific impact. A Latent Factorization of Tensors (LFT) model is one of the most effective models for learning the…

Machine Learning · Computer Science 2025-04-14 Chunyang Zhang , Xin Liao , Hao Wu

Factorization machines and polynomial networks are supervised polynomial models based on an efficient low-rank decomposition. We extend these models to the multi-output setting, i.e., for learning vector-valued functions, with application…

Machine Learning · Statistics 2017-11-07 Mathieu Blondel , Vlad Niculae , Takuma Otsuka , Naonori Ueda

Increasing the batch size is a popular way to speed up neural network training, but beyond some critical batch size, larger batch sizes yield diminishing returns. In this work, we study how the critical batch size changes based on…

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