Search Results for author: Riccardo Di Sipio

Found 6 papers, 6 papers with code

The Dawn of Quantum Natural Language Processing

2 code implementations13 Oct 2021 Riccardo Di Sipio, Jia-Hong Huang, Samuel Yen-Chi Chen, Stefano Mangini, Marcel Worring

In this paper, we discuss the initial attempts at boosting understanding human language based on deep-learning models with quantum computing.

Sentiment Analysis

Bidirectional Long Short-Term Memory (BLSTM) neural networks for reconstruction of top-quark pair decay kinematics

1 code implementation3 Sep 2019 Fardin Syed, Riccardo Di Sipio, Pekka Sinervo

A probabilistic reconstruction using machine-learning of the decay kinematics of top-quark pairs produced in high-energy proton-proton collisions is presented.

High Energy Physics - Experiment Computational Physics Data Analysis, Statistics and Probability

Unfolding as Quantum Annealing

2 code implementations22 Aug 2019 Kyle Cormier, Riccardo Di Sipio, Peter Wittek

High-energy physics is replete with hard computational problems and it is one of the areas where quantum computing could be used to speed up calculations.

Data Analysis, Statistics and Probability High Energy Physics - Experiment Quantum Physics

Shower it again, Pythia

1 code implementation23 May 2019 Riccardo Di Sipio

The Parton-Shower algorithm implement in the Pythia generator is applied multiple times to the same parton-level configuration to estimate the systematic uncertainty affecting large-radius jet substructure variables associated with the stochastic nature of the algorithm.

High Energy Physics - Experiment High Energy Physics - Phenomenology

DijetGAN: A Generative-Adversarial Network Approach for the Simulation of QCD Dijet Events at the LHC

1 code implementation6 Mar 2019 Riccardo Di Sipio, Michele Faucci Giannelli, Sana Ketabchi Haghighat, Serena Palazzo

A Generative-Adversarial Network (GAN) based on convolutional neural networks is used to simulate the production of pairs of jets at the LHC.

High Energy Physics - Experiment High Energy Physics - Phenomenology

Eikos: a Bayesian unfolding method for differential cross-section measurements

1 code implementation6 Aug 2018 Riccardo Di Sipio

A likelihood-based unfolding method based on Bayes' theorem is presented, with a particular emphasis on the application to differential cross-section measurements in high-energy particle interactions.

High Energy Physics - Experiment Data Analysis, Statistics and Probability

Cannot find the paper you are looking for? You can Submit a new open access paper.