Cargando…

Machine learning as an instrument for data unfolding

Detalles Bibliográficos
Autor principal:	Glazov, Alexander
Lenguaje:	eng
Publicado:	2018
Materias:	Machine Learning
Acceso en línea:	http://cds.cern.ch/record/2644126

Ejemplares similares

What is the machine learning.
por: Ostdiek, Bryan
Publicado: (2018)

Machine learning in jet physics
por: Narayana Varma, Sreedevi
Publicado: (2018)

Industry-HEP panel on Machine Learning
por: CERN. Geneva
Publicado: (2017)

Learning New Physics from a machine
por: Wulzer, Andrea
Publicado: (2018)

Machine Learning For Enterprises: Beyond Open Source
por: Puget, Jean-Francois
Publicado: (2018)

Machine Learning for the LHCb Vertex-Locator calibration
por: Majewski, Maciej Witold
Publicado: (2019)

Particle identification at LHCb: new calibration techniques and machine learning classification algorithms
por: Lucio Martinez, Miriam
Publicado: (2018)

DarkMachines
por: Dorigo, Tommaso
Publicado: (2018)

Recent developments in deep-learning applied to open HEP data
por: Strong, Giles Chatham
Publicado: (2018)

Predicting prognosis in patients with stroke treated with intravenous alteplase through blood pressure changes: A machine learning‐based approach
por: Fan, Kaiting, et al.
Publicado: (2023)

Tutorial 2: Scikit-learn
por: Louppe, Gilles
Publicado: (2017)

Anomaly detection with Deep Learning
por: Raval, Siraj
Publicado: (2017)

Guiding New Physics Searches with Unsupervised Learning
por: De Simone, Andrea
Publicado: (2018)

Invited talk: Deep Learning Meets Physics
por: Hochreiter, Sepp
Publicado: (2018)

Direct Learning of Systematics-Aware Summary Statistics
por: De Castro Manzano, Pablo
Publicado: (2018)

A Deep Learning tool for fast simulation
por: Khattak, Gul Rukh
Publicado: (2018)

Deep-learning Top Taggers or The End of QCD?
por: Kasieczka, Gregor
Publicado: (2017)

DeepJet: a deep-learned multiclass jet-tagger for slim and fat jets
por: Gouskos, Loukas, et al.
Publicado: (2018)

Object identification with deep learning using Intel DAAL on Knights Landing processor [Vidyo]
por: Ojika, David Nonso
Publicado: (2017)

Studies to mitigate difference between real data and simulation for jet tagging
por: Buchmuller, Oliver, et al.
Publicado: (2018)

Robust regression and model compression
por: Ivanov, Marian
Publicado: (2018)

ML as a Service for HEP
por: Kuznetsov, Valentin Y
Publicado: (2018)

CWoLa hunting
por: Collins, Jack, et al.
Publicado: (2018)

Convolutional Neural Network for Track Seed Filtering at the CMS HLT
por: Di Florio, Adriano
Publicado: (2018)

HL-LHC tracking challenge
por: Vlimant, Jean-Roch
Publicado: (2018)

Fast calorimeter simulation in LHCb
por: Zakharov, Egor
Publicado: (2018)

Generative Models for Fast Cluster Simulations in the TPC for the ALICE Experiment
por: Deja, Kamil Rafal
Publicado: (2018)

Introduction to the industry session
por: Schramm, Steven Randolph, et al.
Publicado: (2018)

Surrogate Models for Fun and Profit
por: Ustyuzhanin, Andrey
Publicado: (2018)

Adversarial Tuning of Perturbative Parameters in Non-Differentiable Physics Simulators
por: Paganini, Michela
Publicado: (2018)

Welcome
por: Schramm, Steven Randolph, et al.
Publicado: (2018)

Tutorial: Keras/TensorFlow
por: Wunsch, Stefan
Publicado: (2018)

Tutorial: TMVA
por: Moneta, Lorenzo
Publicado: (2018)

Joint Wasserstein GAN contribution
por: Glombitza, Jonas, et al.
Publicado: (2018)

DeepJet: a portable ML environment for HEP
por: mehta, swapneel, et al.
Publicado: (2018)

Searching for new physics without knowing the signal model
por: Amoroso, Simone, et al.
Publicado: (2018)

QCD or What?
por: Thompson, Jennifer
Publicado: (2018)

Invited talk: Overview, RNNs and alike in HEP
por: Cranmer, Kyle Stuart
Publicado: (2018)

Jupyter live demo
por: Forde, Jessica
Publicado: (2018)

Interpreting Deep Neural Networks and their Predictions
por: Samek, Wojciech
Publicado: (2018)

Cannot write session to /tmp/vufind_sessions/sess_shvqk6rjjmkh4tbsv9qknp6li2